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GRAPH NEURAL NETWORKS: TASKS, INFORMATION SECURITY, CYBER SECURITY, AND DIGITAL FORENSICS

ÇİZGE SİNİR AĞLARI: GÖREVLER, BİLGİ GÜVENLİĞİ, SİBER GÜVENLİK VE ADLİ BİLİŞİM

Hamza Talha Gümüş(MİLLİ SAVUNMA ÜNİVERSİTESİ)0000-0001-7360-8138
Can Eyüpoğlu(MİLLİ SAVUNMA ÜNİVERSİTESİ, HAVA HARP OKULU, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-6133-8617
Pages
59–101
Publication Date
2025-08-26
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this study, the tasks and main usage areas of GNNs and their developments in information security, cyber security, and digital forensics are explained.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Graph Neural Networks (GNN) is a deep learning method that belongs to the Artificial Neural Networks (ANN) family and performs information extraction over graphs.
📊
Key Findings & Data:Information security techniques aim to protect the confidentiality, integrity, and access of information against threats and dangers posed by systems and human beings.
💡
Conclusion & Impact:In this study, the tasks and main usage areas of GNNs and their developments in information security, cyber security, and digital forensics are explained.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Çizge Sinir Ağları · Yapay Sinir Ağları · Bilgi Güvenliği

Abstract

Graph Neural Networks (GNN) is a deep learning method that belongs to the Artificial Neural Networks (ANN) family and performs information extraction over graphs. Information security techniques aim to protect the confidentiality, integrity, and access of information against threats and dangers posed by systems and human beings. In terms of cyber security, GNNs are used to prevent and detect attacks on critical infrastructures. Critical infrastructures and systems are gaining importance due to the focus of attention by attackers and the gain in financial and moral losses. In this study, the tasks and main usage areas of GNNs and their developments in information security, cyber security, and digital forensics are explained.

Özet

Çizge Sinir Ağları (Graph Neural Networks-GNN), Yapay Sinir Ağları (Artificial Neural Networks-ANN) ailesine mensup ve çizgeler üzerinden bilgi çıkarımı işlemi gerçekleştiren bir derin öğrenme yöntemidir. Bilgi güvenliği teknikleri ise sistem ve insan olarak adlandırabileceğimiz varlığın tehdit ve tehlike oluşturmasına karşı bilginin gizliliği, bütünlüğü ve erişimine yönelik korumayı amaçlamaktadır. Siber güvenlik açısından ise, GNN’ler kritik altyapılara yönelik saldırıları önlemek ve tespit etmek için kullanılır. Kritik altyapıların ve sistemlerin saldırganlar tarafından ilgi odağı ve maddi-manevi kayıp kazancı sayesinde önem kazanmaktadır. Bu çalışmada GNN’lerin görevleri ve temel kullanım alanları ile birlikte bilgi güvenliği, siber güvenlik ve adli bilişim konularına yönelik gelişmeleri açıklanmaktadır.

🔖

Cite This Article

Gümüş, H. T., Eyüpoğlu, C. (2025). GRAPH NEURAL NETWORKS: TASKS, INFORMATION SECURITY, CYBER SECURITY, AND DIGITAL FORENSICS. İstanbul Ticaret Üniversitesi Teknoloji ve Uygulamalı Bilimler Dergisi, Cilt: 8 Sayı: 1, 59–101. https://doi.org/10.56809/icujtas.1562430

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References (275)

  1. Abu-El-Haija, S., Kapoor, A., Perozzi, B., Lee, J. (2018). N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. In Proceedings of the 32nd International Conference on Neural Information Processing System, December, pp. 9198-9208.
  2. ACM. (2017). Cybersecurity Curricula 2017: Curriculum Guidelines for Post-Secondary Degree Programs in Cybersecurity. Association for Computing Machinery, New York, NY, United States, ISBN: 978-1-4503-8919-8.
  3. ACM. (2020). Cybersecurity Curricular Guidance for Associate-Degree Programs. Association for Computing Machinery, New York, NY, United States, ISBN: 978-1-4503-7555-9.
  4. Ahi, M.G. (2009). Adli Bilişim Nedir?. Güncel Hukuk Dergisi, Mart 2009.
  5. Alom, Z., Hasan, M., Yakopcic, C., Taha, T.M. (2015). Inception Recurrent Convolutional Neural Network for Object Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June.
  6. Alom M.Z., Sidike P., Hasan M., Taha T.M., Asari V.K. (2017). Handwritten Bangla Character Recognition Using the State-of-the-Art Deep Convolutional Neural Networks. Computational Intelligence and Neuroscience, vol. 2018, no. 1, p. 6747098.
  7. Antoniadis, P., Filntisis, P.P., Maragos, P. (2021). Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition. In Proceedings of the 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition.
  8. Anwaar, M.U., Han, Z., Arumugaswamy, S., Khan, R.A., Weber, T., Qiu, T., Shen, H., Liu, Y., Kleinsteuber, M. (2020). Metapath- and Entity-aware Graph Neural Network for Recommendation. In Proceedings of the 1st Workshop on Multimedia Computing towards Fashion Recommendation, October, pp. 3-10.
  9. Arya, D., Gupta, D.K., Rudinac, S., Worring, M. (2020). HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs. arXiv:2010.04558.
  10. Ashraf, A.W.U., Budka, M., Musial, K. (2019). Simulation and Augmentation of Social Networks for Building Deep Learning Models. Future Internet, vol. 14, no. 3, p. 93.
  11. Aslay, F. (2017). Siber Saldırı Yöntemleri ve Türkiye’nin Siber Güvenlik Mevcut Durum Analizi. International Journal of Multidisciplinary Studies and Innovative Technologies, vol. 1, no. 1, pp. 24-28.
  12. Atwood, J., Towsley, D. (2016). Diffusion-Convolutional Neural Networks. In Proceedings of the 29th Conference on Neural Information Processing Systems, Barcelona, Spain, pp. 1993-2001.
  13. Aykas, D., Mehrkanoon, S. (2021). Multistream Graph Attention Networks for Wind Speed Forecasting. In Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence, pp. 1-8.
  14. Bai, H., Chan, S.H.G. (2021). Motion-guided Non-local Spatial-Temporal Network for Video Crowd Counting. arXiv:2104.13946.
  15. Beierl, N. (2021). NoSyms: A neural network approach to detecting data structures in raw memory. OpenAIRE, doi: 10.5281/zenodo.4977243.
  16. Beishuizen, M. (2018). Structural graph learning in real-world digital forensics. Master’s Thesis, Artificial Intelligence, University of Groningen, Netherlands.
  17. Berrendorf, M., Faerman, E., Melnychuk, V., Tresp, V., Seidl, T. (2020). Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned. In Proceedings of the Advances in Information Retrieval: 42nd European Conference on IR Research, Lisbon, Portugal, April 14-17, pp. 3-11.
  18. BGA Security (2011). Bilişim Sistemlerinde Adli Bilişim Analizi ve Bilgisayar Olayları İnceleme. https://www.bgasecurity.com/makale/bilisim-sistemlerinde-adli-bilisim-analizi-ve-bilgisayar-olaylari-inceleme/
  19. Bhattacharya, U., Mittal, T., Chandra, R., Randhavane, T., Bera, B., Manocha, D. (2019). STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 2, pp. 1342-1350.
  20. Bıçakçı, S., Ergun, F.D., Çelikpala, M. (2016). Türkiye’de Siber Güvenlik. Türkiye’de Siber Güvenlik ve Nükleer Enerji, pp. 28-73.
  21. Bianco, S. (2017). Large age-gap face verification by feature injection in deep networks. Pattern Recognition Letters, vol. 90, pp. 36-42.
  22. Bratholm, L.A., Gerrard, W., Anderson, B., Bai, S., Choi, S., Dang, L.,. Glowacki, D.R. (2021). A community-powered search of machine learning strategy space to find NMR property prediction models. PLOS ONE, vol. 16, no. 7, p. e0253612.
  23. Broccatelli, F., Trager, R., Reutlinger, M., Karypis, G., Li, M. (2022). Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces. Molecular Informatics, vol. 41, no. 8, p. 2100321. Cachay, S.R., Ramesh, V., Cole, J.N.S., Barker, H., Rolnick, D. (2021). ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models. NeurIPS Datasets and Benchmarks. arXiv:2111.14671.
  24. Cai, D., Lam, W. (2020). Graph Transformer for Graph-to-Sequence Learning. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 5, pp. 7464-7471.
  25. Caramalau, R., Bhattarai, B., Kim, T.K. (2021). Sequential Graph Convolutional Network for Active Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9583-9592.
  26. CBDDO. (2020). Bilgi ve İletişim Güvenliği Rehberi. Türkiye Cumhuriyeti Cumhurbaşkanlığı Dijital Dönüşüm Ofisi.
  27. Cetoli, A., Bragaglia, S., O’Harney, A.D., Sloan M. (2017). Graph Convolutional Networks for Named Entity Recognition. In Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories, Prague, Czech Republic, pp. 37-45.
  28. Chaiang, W.L., Liu, X., Si, S., Li, Y, Bengio, S., Hsieh, C.J. (2019). Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, July 2019, pp. 257-266.
  29. Chami, I., Ying, R., Re, C., Leskovec, J. (2019). Hyperbolic Graph Convolutional Neural Networks. In Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, Canada, pp. 1-12.
  30. Chang, S., Park, S.U., Yang, J., Kwak, N. (2019). Sym-Parameterized Dynamic Inference for Mixed-Domain Image Translation. In Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, pp. 4802-4810.
  31. Chang, L., Branco, P. (2021). Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT Algorithms. arXiv:2111.13597.
  32. Chen, L.C., Papandreou, G., Kokkinos, K., Murphy, K., Yuille, A.L. (2017a). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 834-848.
  33. Chen, J., Yuan, C., Deng, M., Tao, C., Peng, J., Li, H. (2017b). On the Selective and Invariant Representation of DCNN for High-Resolution Remote Sensing Image Recognition. arXiv:1708.01420.
  34. Chen, K., Lin, Z., Wan, J., Xu, L., Xu, C. (2019a). Multi-owner Secure Encrypted Search Using Searching Adversarial Networks. In Proceedings of the International Conference on Cryptology and Network Security, pp. 184-195.
  35. Chen, C., Hu, S., Nikdel, P., Mori, G., Sava, M. (2019b). Relational Graph Learning for Crowd Navigation. In Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 10007-10013.
  36. Chen, B., Barzilay, R., Jaakkola, T. (2019c). Path-Augmented Graph Transformer Network. arXiv:1905.12712.
  37. Chen, Z., Wei, X., Wang, P., Guo, Y. (2019d). Multi-Label Image Recognition with Graph Convolutional Networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5177-5186.
  38. Chen, L., Li, Z., Wang, Y., Xu, T., Wang, Z., Chen, E. (2020a). MMEA: Entity Alignment for Multi-Modal Knowledge Graphs. In Proceedings of the 13th International Conference on Knowledge Science, Engineering and Management, Hangzhou, China, August 28-30, pp. 134-147.
  39. Chen, L., Wu, L., Hong, R., Zhang, K., Wang, M. (2020b). Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, pp. 27-35.
  40. Chen, X., Morehead, M., Liu, J., Cheng, J. (2021). DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment. arXiv:2205.10627.
  41. Choi, J., Choi, H., Hwang, J., Park, N. (2021). Graph Neural Controlled Differential Equations for Traffic Forecasting. arXiv:2112.03558.
  42. Craandijk, D., Bex, F. (2020). Deep Learning for Abstract Argumentation Semantics. Craandijk, D., Bex, F. (2020). Deep Learning for Abstract Argumentation Semantics. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp. 1667-1673.
  43. Creagh, A.P., Lipsmeier, F., Lindemann, M., Vos, M.D. (2021). Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones. Scientific Reports, vol. 11, no. 1, p. 14301.
  44. Cucurull, G., Taslakian, P., Vazquez, D. (2019). Context-Aware Visual Compatibility Prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12617-12626.
  45. Cui, S., Yu, B., Liu, T., Zhang, Z.Y., Wang, X., Shi, J. (2020). Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation. arXiv:2002.10757.
  46. Dasgupta, S., Piplai, A., Ranade, P., Joshi, A. (2021). Cybersecurity Knowledge Graph Improvement with Graph Neural Networks. In Proceedings of the 2021 IEEE International Conference on Big Data, pp. 3290-3297.
  47. Dasoulas, G., Scaman, K., Virmaux, A. (2021). Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks. In Proceedings of the International Conference on Machine Learning, pp. 2456-2466.
  48. Degardin, B., Neves, J., Lopes, V., Brito, J., Yaghoubi, E., Proença, H. (2021). Generative Adversarial Graph Convolutional Networks for Human Action Synthesis. In Proceedings of the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2753-2762.
  49. Dehmamy, N., Barabasi, A.L., Yu, R. (2019). Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology. In Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, Canada, pp. 1-11.
  50. Dewan, A., Burgard, W. (2020). DeepTemporalSeg: Temporally Consistent Semantic Segmentation of 3D LiDAR Scans. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation, pp. 2624-2630.
  51. Ding, J., Li, A., Hu, Z., Wang, L. (2017). Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks. arXiv:1706.04303.
  52. Dong, S., Li, R. (2019). Traffic identification method based on multiple probabilistic neural network model. Neural Computing and Applications, vol. 31, pp. 473-487.
  53. Dong, H., Chen, J., Feng, F., He, X., Bi, S., Ding, Z., Cui, P. (2020). On the Equivalence of Decoupled Graph Convolution Network and Label Propagation. In Proceedings of the Web Conference 2021, New York, NY, USA, pp. 3651-3662.
  54. Dou, Y., Shu, K., Xia, C., Yu, P.S., Sun, L. (2021). User Preference-aware Fake News Detection. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2051-2055.
  55. Dutta, S., Ma, L., Saha, T.K., Lu, D., Tetreault, J., Jaimes, A. (2021). GTN-ED: Event Detection Using Graph Transformer Networks. arXiv:2104.15104.
  56. Dülger, M.V. (2017). Adli Bilişim & Ülkemizde Uygulaması. H+ Dergi, Haziran 2017, pp. 20-25.
  57. Dwivedi, V.P., Bresson, X. (2021). A Generalization of Transformer Networks to Graphs. arXiv:2012.09699.
  58. Elinas, P., Bonilla, E.V., Tiao, L. (2020). Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings. Advances in Neural Information Processing Systems, vol. 33, pp. 18648-18660.
  59. Engelcke, M., Kosiorek, A.R., Parker Jones, O., Posner, I. (2020). GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations. arXiv:1907.13052.
  60. Erharter, G.H., Hansen, T.F., Liu, Z., Marcher, T. (2021). Reinforcement learning based process optimization and strategy development in conventional tunneling. Automation in Construction, vol. 127, p. 103701.
  61. Fan, Y., Ju, M., Hou, S., Ye, Y., Wan, W., Wang, K., Mei, Y., Xiong, Q. (2021). Heterogeneous Temporal Graph Transformer: An İntelligent System For Evolving Android Malware Detection. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, August, pp. 2831-2839.
  62. Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y. (2018). Hypergraph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 3558-3565.
  63. Feng, F., He, X., Tang, J., Chua, T.S. (2019). Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure. IEEE Transactions on Knowledge and Data Engineering vol. 33, pp. 2493-2504.
  64. Gao, J., Zhang, T., Xu, C. (2019a). Graph Convolutional Tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4649-4659. Gao, H., Chen, Y., Ji, S. (2019b). Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations. In Proceedings of the World Wide Web Conference, pp. 2743-2749.
  65. Gao, Z., Tan, C., Li, S.Z. (2022). AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB. arXiv:2202.01079.
  66. Garrido, J.S., Dold, D., Frank, J. (2021). Machine learning on knowledge graphs for context-aware security monitoring. In Proceedings of the 2021 IEEE International Conference on Cyber Security and Resilience, pp. 55–60.
  67. Ge, Z.Y., Bewlwy, A., Mccool, C., Upcroft, B., Corke, P., Sanderson, C. (2016). Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks. In Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision, pp. 1-6.
  68. Ghorbani, M., Baghshah, M.S., Rabiee, H.R. (2018). MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 208-211.
  69. Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E. (2017). Neural Message Passing for Quantum Chemistry. In Proceedings of the International Conference on Machine Learning, pp. 1263-1272.
  70. Gokden, B. (2021). Power Law Graph Transformer for Machine Translation and Representation Learning. arXiv:2107.02039.
  71. Guo, Z., Tang, L., Guo, T., Yu, K., Alazab, M., Shalaginov, A. (2021). Deep Graph neural network-based spammer detection under the perspective of heterogeneous cyberspace. Future Generation Computer Systems, vol. 117, pp. 205-218.
  72. Gümüş, H.T., Eyüpoğlu, C. (2023). Grafik Sinir Ağlarına Genel Bir Bakış. EMO Bilimsel Dergi, vol. 13, no. 2, pp. 39-56.
  73. Gümüş, H.T., Eyüpoğlu, C. (2024). Grafik Sinir Ağları Üzerine Bir İnceleme. Savunma Bilimleri Dergisi, vol. 20, no. 1, pp. 105-138.
  74. Hamilton, W.L., Ying, R., Leskovec, L. (2017). Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA, pp. 1-11.
  75. Han, Y., Karunasekeran, S., Leckie, C. (2020) Graph Neural Networks with Continual Learning for Fake News Detection from Social Media. arXiv:2007.03316.
  76. Han, X. (2017). MR-based synthetic CT generation using a deep convolutional neural network method. Medical Physics, vol. 44, no. 4, pp. 1408-1419.
  77. Harley, A.W., Derpanis, K.G., Kokkinos, I. (2015). Learning Dense Convolutional Embeddings for Semantic Segmentation. arXiv:1511.04377.
  78. He, J., Erfani, S., Wijewickrema, S., O’Leary, S., Ramamohanarao, K. (2020). Learning Non-Unique Segmentation with Reward-Penalty Dice Loss. In Proceedings of the 2020 International Joint Conference on Neural Networks, pp. 1-8.
  79. Hejazi, F., Vuckovic, K., Rahnavard, N. (2021). DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural Networks. In Proceedings of the IEEE INFOCOM 2021 - IEEE Conference on Computer Communications, pp. 1-9.
  80. Huang, J., Shen, H., Hou, L., Cheng, X. (2019). Signed Graph Attention Networks. In Proceedings of the Artificial Neural Networks and Machine Learning–ICANN 2019: Workshop and Special Sessions: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, pp. 566-577.
  81. Huang, Z., Chen, X., Wang, B. (2023). DHSEGATs: Distance and Hop-wise Structures Encoding Enhanced Graph Attention Networks. Journal of Systems Engineering and Electronics, vol. 34, no. 2, pp. 350-359.
  82. Huo, Y., Xu, Z., Bao,S., Bermudez, C., Plassard, A.J., Liu, J., Yao, Y., Assad, A., Abramson, R.G., Landman, B.A. (2018a). Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks. Medical Imaging 2018: Image Processing, vol. 10574, pp. 45-51.
  83. Huo, Y., Xu, Z., Moon, H., Bao, S., Assad, A., Moyo, T.K., Savona, M.R., Abramson, R.G., Landman, B.A. (2018b). SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth. IEEE Transactions on Medical Imaging, vol. 38, no. 4, pp. 1016-1025.
  84. Hussain, S., Anwar, S.M., Majid, M. (2018). Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network. Neurocomputing, vol. 282, pp. 248-261.
  85. Hussain, S., Zaki, M.J., Subramanian, D. (2021). Edge-a ugmented Graph Transformers: Global Self-attention is Enough for Graphs. arXiv:2108.03348.
  86. Iscen, I., Tolias, G., Avrithis, Y., Chum, O., Schmid, C. (2020). Graph convolutional networks for learning with few clean and many noisy labels. In Proceedings of the Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, pp. 286-302.
  87. ISO27001. (2017). TS ISO/IEC 27001:2017.
  88. Jafari, M., Auer, D., Francis, S., Garibaldi, J., Chen, X. (2020). DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation. In Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging, pp. 1144-1148.
  89. Jafari, M., Li, R., Xing, Y., Auer, D., Francis, S., Garibaldi, J., Chen, X. (2019). FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net. In Proceedings of the 10th International Conference on Image and Graphics, Beijing, China, August 23-25, pp. 529-537.
  90. Jangda, A., Polisetty, S., Guha, A., Serafini, M. (2020). Accelerating graph sampling for graph machine learning using GPUs. In Proceedings of the Sixteenth European Conference on Computer Systems, pp. 311-32. Jia, X., Liu, S., Feng X., Zhang, L. (2019). FOCNet: A Fractional Optimal Control Network for Image Denoising. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6047-6056.
  91. Jia, Z., Lin, Y., Wang, J., Feng, Z., Xie, X., Chen, C. (2021). HetEmotionNet: Two-Stream Heterogeneous Graph Recurrent Neural Network for Multi-modal Emotion Recognition. In Proceedings of the 29th ACM International Conference on Multimedia, pp. 1047-1056.
  92. Jia, X., Wu, P., Chen, L., Li, H., Liu, Y.S., Yan, J. (2023). HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 11, pp. 13860-13875.
  93. Jiang, B., Lin, D. (2018a). Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised Learning. arXiv:1809.09839.
  94. Jiang, J., Li, J. (2018b). Constructing Financial Sentimental Factors in Chinese Market Using Natural Language Processing. arXiv:1809.0839.
  95. Jiang, J., Chen, J., Choo, KK.R., Liu, C., Liu, K., Yu, M. (2018c). A Visualization Scheme for Network Forensics Based on Attribute Oriented Induction Based Frequent Item Mining and Hyper Graph. In: Matoušek, P., Schmiedecker, M. (eds) Digital Forensics and Cyber Crime. ICDF2C 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 216, pp. 130-143.
  96. Jiang, S., Balaprakash, P. (2020). Graph Neural Network Architecture Search for Molecular Property Prediction. In Proceedings of the 2020 IEEE International Conference on Big Data, pp. 1346-1353.
  97. Jiang, S., Sun, B., Wang, L., Bai, Y., Li, K., Fu, Y. (2021). Skeleton Aware Multi-modal Sign Language Recognition. In Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 3408-3418.
  98. Kanezaki, A., Matsushita, Y., Nishida, Y. (2018). RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5010-5019.
  99. Karamitsos, I., Afzulpurkar, A., Trafalis, T.B. (2020). Malware Detection for Forensic Memory Using Deep Recurrent Neural Networks. Journal of Information Security, vol. 11, no. 2, pp. 103-120.
  100. Kassani, S.H., Kassani, P.H., Wesolowski, M.J., Schneider, K.A., Deters, R. (2019). Breast Cancer Diagnosis with Transfer Learning and Global Pooling. In Proceedings of the 2019 International Conference on Information and Communication Technology Convergence, pp. 519-524.
  101. Ke, L., Tai, Y.W., Tang, C.K. (2021). Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4019-4028.
  102. Kemker, R., Salvaggio, C., Kanan, C. (2018). Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 145, pp. 60-77.
  103. Kessler, G.C., Ramsay, J.D. (2014). A Proposed Curriculum in Cybersecurity Education Targeting Homeland Security Students. In Proceedings of the 47th Hawaii International Conference on System Science, pp. 4932-4937.
  104. Kim, H.H., Yu, S., Tomasi, C. (2021). Joint Detection of Motion Boundaries and Occlusions. arXiv:2111.01261.
  105. Kim, J., Kim, K. (2021). Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network. In Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference, pp. 990-995.
  106. Kipf, T.N., Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations, Toulon, France, April 24-26, pp. 1-14.
  107. Klicpera, J., Groß, J., Günnemann, S. (2020). Directional Message Passing for Molecular Graphs. arXiv:2003.03123.
  108. Kooverjee, K., James, S., Zyl, T.V. (2022). Investigating Transfer Learning in Graph Neural Networks. Electronics, vol. 11, no. 8, p. 1202.
  109. Kortylewski, A., Egger, B., Schneider, A., Gerig, T., Forster, A.M., Vetter, T. (2018). Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2093-2102.
  110. Kortylewski, A., He, J., Liu, Q., Yuille, A. (2020). Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8940-8949.
  111. Kumar, P., Grewal, M., Srivastava, M.M. (2018). Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs. In Proceedings of the International Conference on Image Analysis and Recognition, pp. 546-552.
  112. Kumar, A., Morabia, K., Wang, J., Chang, K.C.C., Schwing, A. (2022). CoVA: Context-aware Visual Attention for Webpage Information Extraction. In Proceedings of the Fifth Workshop on e-Commerce and NLP, pp. 80-90.
  113. Lai, P., Lu, Z. (2021). BERT-GT: Cross-sentence n-ary relation extraction with BERT and Graph Transformer. Bioinformatics, vol. 36, no. 24, pp. 5678-5685.
  114. Lamb, G., Paige, B. (2020). Bayesian Graph Neural Networks for Molecular Property Prediction. arXiv:2012.02089
  115. Lee, J., Park, J., Kim, K.L., Nam, J. (2017). Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms. arXiv:1703.01789.
  116. Lee, Y., Chung, J., Rhu, M. (2022). SmartSAGE: training large-scale graph neural networks using in-storage processing architectures. In Proceedings of the 49th Annual International Symposium on Computer Architecture, New York, NY, USA, pp. 932-945.
  117. Leng, Y., Ruiz, R., Dong, X., Pentland, A. (2020). Interpretable Recommender System With Heterogeneous Information: A Geometric Deep Learning Perspective. SSRN Electronic Journal, vol. J, no. 10, pp. 2411-2430.
  118. Li, Y., Tarlow, D., Brockschmidt, M., Zemel, R. (2016). Gated graph sequence neural networks. In Proceedings of the 5th International Conference on Learning Representations.
  119. Li, H., Dou, X., Tao, C., Hou, Z., Chen, J., Peng, J., Deng, M., Zhao, L. (2017). RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data. arXiv:1705.10450.
  120. Li, R., Wang, S., Zhu, F., Huang, J. (2018). Adaptive Graph Convolutional Neural Networks. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1, pp. 3546-3553.
  121. Li, G., Muller, M., Thabet, A., Ghanem, B. (2019a). DeepGCNs: Can GCNs Go as Deep as CNNs?. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9267-9276.
  122. Li, C., Cao, Y., Hou, L., Shi, J., Li, J., Chua, T.S. (2019b). Semi-supervised Entity Alignment via Joint Knowledge Embedding Model and Cross-graph Model. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong, November 3-7, pp. 2723-2732.
  123. Li, Z., Zhang, L., Song, G. (2019c). GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks. arXiv:1902.09817.
  124. Li, Y., Tam, D.S., Xie, S., Liu, X., Ying, Q., Lau, W., Chiu, D., Chen, S.Z. (2020a). GTEA: Representation Learning for Temporal Interaction Graphs via Edge Aggregation. arXiv:2009.05266.
  125. Li, R., Auer, D., Wagner, C., Chen, X. (2020b). A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation. In Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging, pp. 1168-1172. Li, X., Ling, H. (2021a). TransCamP: Graph Transformer for 6-DoF Camera Pose Estimation. arXiv:2105.14065.
  126. Li, Z., Jin, X., Li, W., Guan, S., Guo, J., Shen, H., Wang, Y., Cheng, X. (2021b). Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 408-417.
  127. Liang, B., Yin, R., Gui,L., Du, J., Xu, R. (2020). Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis. In Proceedings of the 28th International Conference on Computational Linguistics, pp. 150-161.
  128. Lin, C., Su, H., Tung, S., Hsu, W.H. (2021). Multivariate and Propagation Graph Attention Network for Spatial-Temporal Prediction with Outdoor Cellular Traffic. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3248-3252.
  129. Liu, L., Songi X., Wang, M., Liu, Y., Zhang, L. (2021a). Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12737-12746.
  130. Liu, Y., Tu, W., Zhou, S., Liu, X., Song, L., Yang, X., Zhu, E. (2021b). Deep Graph Clustering via Dual Correlation Reduction. arXiv:2112.14772.
  131. Liu, Y., Pan, S., Wang, Y.G., Xiong, F., Wang, L., Chen, Q., Lee, V.C.S. (2021c). Anomaly Detection in Dynamic Graphs via Transformer. IEEE Transactions on Knowledge & Data Engineering, vol. 35, no. 12, pp. 12081-12094.
  132. Liu, X., Yang, H., Chen, Q., Zhang, R., Yang, L., Xiao, B., Wang, C. (2022a). FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance. Capital Markets: Market Microstructure eJournal.
  133. Liu, L., Tsai, W.T., Bhuiyan, Z.A., Peng, H., Liu, M. (2022b). Blockchain-enabled fraud discovery through abnormal smart contract detection on Ethereum. Future Generation Computer Systems, vol. 128, pp. 158-166.
  134. Marafioti, A., Holighaus, N., Majdak, P., Perraudin, N. (2018). Audio inpainting of music by means of neural networks. arXiv:1810.12138.
  135. Mardanisamani, S., Maleki, F., Kassani, S.H., Rajapaksa, S., Duddu, H., Wang, M., Shirtliffe, S., Ryu, S., Josuttes, A., Zhang, T., Vail, S., Pozniak, C., Parkin, I., Stavness, I., Eramian, M. (2019). Crop Lodging Prediction From UAV-Acquired Images of Wheat and Canola Using a DCNN Augmented With Handcrafted Texture Features. In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA, pp. 2657-2664.
  136. Martin, D.S., Paez, J.G., Diaz, J.B. (2021). A stacked deep convolutional neural network to predict the remaining useful life of a turbofan engine. arXiv:2111.12689.
  137. Masoumian, A., Rashwan, H.A., Abdulwahab, S., Cristiano, J., Puig, D. (2023). GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network. Neurocomputing, vol. 517, pp. 81-92.
  138. Min, Y., Wenkel, F., Wolf, G. (2020). Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks. Advances in Neural Information Processing Systems, vol. 33, pp. 14498-14508.
  139. Min, S.W., Wu, K., Huang, S., Hidayetoğlu, M., Xiong, J.J., Ebrahimi, E., Chen, D., Hwu, W.M. (2021). Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture. arXiv:2103.03330.
  140. Mironov, A., Khuziev, I. (2022). Optimization of Decision Tree Evaluation Using SIMD Instructions. In Proceedings of the VLDB Endowment, vol. 14, no. 11, pp. 2087-2100.
  141. Mondal, S., Manasi, S.D., Kunal, K., Sapatnekar, S.S. (2022). GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching. In Proceedings of the 59th ACM/IEEE Design Automation Conference, pp. 565-570.
  142. Monti, F., Boscaini, D., Masci, J., Rodolà, E., Svoboda, J., Bronstein, M.M. (2017). Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 5425-5434.
  143. Morehead, A., Chen, C., Cheng, J. (2022). Geometric Transformers for Protein Interface Contact Prediction. In Proceedings of the International Conference on Learning Representation, pp. 1-18.
  144. Mou, Z., Gao, F., Liu, J., Wu, Q. (2021). Resilient UAV Swarm Communications with Graph Convolutional Neural Network. IEEE Journal on Selected Areas in Communications. vol. 40, no. 1, pp. 393-411.
  145. Murugesan, K., Atzeni, M., Kapanipathi, P., Shukla, P., Kumaravel, S., Tesauro, G., Talamadupula, K., Sachan, M., Campbell, M. (2021). Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 10, pp. 9018-9027.
  146. Nguyen, V.A., Nguyen, D.Q., Nguyen, V., Le, T., Tran, Q.H., Phung, D. (2022). ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings, pp. 178-182.
  147. Nt, H., Jin, C.J., Murata, T. (2019). Learning Graph Neural Networks with Noisy Labels. In Proceedings of the International Conference on Learning Representation, pp. 1-5.
  148. Oh, J., Cho, K., Bruna, J. (2019). Advancing GraphSAGE with A Data-Driven Node Sampling. In Proceedings of the International Conference on Learning Representation, pp. 1-6.
  149. Oono, K., Suzuki, T. (2020). Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. In Proceedings of the International Conference on Learning Representation, pp. 1-37.
  150. Önaçan, M.B.K., Atan, H. (2016). Siber Güvenlikte Lisansüstü Eğitim: Deniz Harp Okulu Örneği. Trakya University Journal of Engineering Sciences, vol. 17, no. 1, pp. 13-21.
  151. Palowitch, J., Perozzi, B. (2019). MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit. arXiv:1909.11793.
  152. Pan, S.Y., Lu, C.Y., Lee, S.P., Peng, W.H. (2021). Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks. In Proceedings of the 2021 IEEE International Conference on Multimedia and Expo, pp. 1-6.
  153. Panda, M. (2016). Towards the effectiveness of Deep Convolutional Neural Network based Fast Random Forest Classifier. arXiv:1609.08864.
  154. Pande, A., Ni, K., Kini, V. (2019). SWAG: Item Recommendations using Convolutions on Weighted Graphs. In Proceedings of the 2019 IEEE International Conference on Big Data, pp. 2903-2912.
  155. Parisot, S., Ktena, S.I., Ferrante, E., Lee, M., Moreno, R.G., Glocker, B., Rueckert, D. (2017). Spectral Graph Convolutions for Population-based Disease Prediction. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention.
  156. Peng,E., Hong, X., Chen, H., Zhao, G. (2019). Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 03, pp. 2669-2676.
  157. Praramadhan, A.A., Saputra, G.E. (2021). Cycle Generative Adversarial Networks Algorithm With Style Transfer For Image Generation. arXiv:2101.03921.
  158. Protogerou, A., Papadopoulos, S., Drosou, A., Tzovaras, D., Refanidis, I. (2021). A Graph Neural Network Method for Distributed Anomaly Detection in IoT. Evolving Systems, vol. 12, no. 1, pp. 19-36.
  159. Qian, G., Abualshour, A., Li, G., Thabet, A., Ghanem, B. (2021). PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11683-11692.
  160. Qiu, L., Zhang, X., Li, Y.R., Li, G., Wu, X.J., Xiong, Z., Han, X., Cui, S. (2020). Peeking into occluded joints: A novel framework for crowd pose estimation. In Proceedings of the 16th European Conference on Computer Vision, Glasgow, UK, August 23-28, pp. 488-504.
  161. Ragi, S., Rahman, H., Duckworth, J., Jawaharraj, K., Chundi, P., Gadhamshetty, V. (2021). Artificial Intelligence-driven Image Analysis of Bacterial Cells and Biofilms. IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 20, no. 1, pp. 174-184.
  162. Rajput, N.K., Grover, B.A., Rathi, V.K. (2020). Word frequency and sentiment analysis of twitter messages during Coronavirus pandemic. arXiv:2004.03925.
  163. Rampášek, L., Galkin, M., Dwivedi, V.P., Luu, A.T., Wolf, G., Beaini, D. (2022). Recipe for a General, Powerful, Scalable Graph Transformer. Advances in Neural Information Processing Systems, vol. 35, pp. 14501-14515.
  164. Roopak, M., Tian, G.Y., Chambers, J. (2019). Deep Learning Models for Cyber Security in IoT Networks. In Proceedings of the IEEE 9th Annual Computing and Communication Workshop and Conference, pp. 0452-0457.
  165. Sağıroğlu, Ş., Alkan, M. (2018). Siber Güvenlik ve Savunma Farkındalık ve Caydırıcılık. Grafiker Yayınları, no. 287, Ankara, Türkiye.
  166. Sahla, G., Hennequin, R., Vazirgiannis, M. (2019). Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks. In Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, Canada, pp. 1-8.
  167. Sahla, G., Hennequin, R., Vazirgiannis, M. (2020). Simple and Effective Graph Autoencoders with One-Hop Linear Models. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, Ghent, Belgium, September 14-18, pp. 319-334.
  168. Sajadmanesh, S., Gatica-Perez, D. (2021). Locally Private Graph Neural Networks. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, November, pp. 2130-2145.
  169. Schelkoph, D.J., Peterson, G.L., Okolica, J.S. (2019). Digital Forensics Event Graph Reconstruction. In Proceedings of the 10th International EAI Conference on Digital Forensics and Cyber Crime, New Orleans, LA, USA, September 10-12, pp. 185-203.
  170. Schuler, J.P.S., Romani, S., Abdel-Nasser, M., Rashwan, H., Puig, D. (2021). Reliable Deep Learning Plant Leaf Disease Classification Based on Light-Chroma Separated Branches. Artificial Intelligence Research and Development, pp. 375-382.
  171. Schuler, J.P.S., Romani, S., Abdel-Nasser, M., Rashwan, H., Puig, D. (2022). Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks. Mendel, vol. 28, no. 1, pp. 23-31.
  172. Securisis, 2022, securisis.com
  173. Sengupta, S., Kambhampati, S. (2020). Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense. arXiv:2007.10457.
  174. Sengupta, S., Kambhampati, S. (2021). Learning Movement Strategies for Moving Target Defense. In Proceedings of the International Conference on Learning Representation, pp. 1-18.
  175. Shang, C., Tang, Y., Huang, J., Bi, J., He, X., Zhou, B.W. (2018). End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 3060-3067.
  176. Shi, L., Zhang, Y., Cheng, J., Lu, H. (2019a). Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks. IEEE Transactions on Image Processing, vol. 29, pp. 9532-9545.
  177. Shi, L., Zhang, Y., Cheng, J., Lu, H. (2019b). Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12026-12035.
  178. Shi, Y., Huang, Z., Feng, S., Zhong, H., Wang, W., Sun, Y. (2020). Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification. arXiv:2009.03509.
  179. Shi, Y., Huang, Z., Feng, S., Zhong, H., Wang, W., Sun, Y. (2021a). Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification. arXiv:2009.03509.
  180. Shi, Y., Zhang, S., Zhou, C., Liang, X., Yang, X., Lin, L. (2021b). GTAE: Graph-Transformer based Auto-Encoders for Linguistic-Constrained Text Style Transfer. ACM Transactions on Intelligent Systems and Technology, vol. 12, no. 3, pp. 1-16.
  181. Simou, S., Kallıniatas, C., Gritzalis, S., Mouratidis, H. (2016). A survey on cloud forensics challenges and solutions. Security and Communication Networks, vol. 9, no. 18, p. 6285-6314.
  182. Sinha, K., Sodhani, S., Pineau, J., Hamilton, W.L. (2020). Evaluating Logical Generalization in Graph Neural Networks. arXiv:2003.06560.
  183. Sofianos, T., Sampieri, A., Franco, L., Galasso, F. (2021). Space-Time-Separable Graph Convolutional Network for Pose Forecasting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11209-11218.
  184. Song, W., Yin, H., Liu, C., Song, D. (2018). DeepMem: Learning Graph Neural Network Models for Fast and Robust Memory Forensic Analysis. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, pp. 606-618.
  185. Su, C., Zhang, S., Xing, J., Gao, W., Tian, Q. (2016). Deep Attributes Driven Multi-Camera Person Re-identification. In Proceedings of the 14th European Conference on Computer Vision, October 11-14, pp. 475-491.
  186. Sun, L., Zhao, C., Stolkin, R. (2017). Weakly-supervised DCNN for RGB-D Object Recognition in Real-World Applications Which Lack Large-scale Annotated Training Data. arXiv:1703.06370.
  187. Taguchi, H., Liu, X., Murata, T. (2021). Graph Convolutional Networks for Graphs Containing Missing Features. Future Generation Computer Systems, vol. 117, pp. 155-168.
  188. Tai, C.Y., Wu, M.R., Chu, Y.W., Chu, S.Y., Ku, L.W. (2020). MVIN: Learning Multiview Items for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, July, pp. 99-108.
  189. Tallec, G., Dapogny, A., Bailly, K. (2022). Multi-Order Networks for Action Unit Detection. IEEE Transactions on Affective Computing, vol. 14, no. 4, pp. 2876-2888.
  190. Tam, D.S., Lau, W., Hu, B., Ying, Q., Chiu, D., Liu, H. (2019). Identifying Illicit Accounts in Large Scale E-payment Networks - A Graph Representation Learning Approach. arXiv:1906.05546.
  191. Tang, Y., Ma, L., Zhou, L. (2019). Hallucinating Optical Flow Features for Video Classification. arXiv:1905.11799.
  192. TDK Sözlük, https://sozluk.gov.tr/
  193. Teepe, T., Khan, A., Gilg, J., Herzog, F., Hörmann, S., Rigoll, G. (2021). GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition. In Proceedings of the 2021 IEEE International Conference on Image Processing, pp. 2314-2318.
  194. Thakoor, S., Tallec, C., Azar,M.G., Azabou, M., Dyer, E.L., Munos, R., Veličković, P., Valko, M. (2022). Large-Scale Representation Learning on Graphs via Bootstrapping. In Proceedings of the International Conference on Learning Representation, pp. 1-21.
  195. Theodoropoulos, C., Henderson, J., Coman, A.C., Moens, M.F. (2021). Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learning. arXiv:2109.00840.
  196. Vashishth, S. (2019). Neural Graph Embedding Methods for Natural Language Processing. arXiv:1911.03042.
  197. Vashishth, S., Sanyal, S., Nitin, V., Talukdar, P. (2020). Composition-Based Multi-Relational Graph Convolutional Networks. In Proceedings of the International Conference on Learning Representation, pp. 1-15.
  198. Veličković, P., Cucurull G., Casanova A., Romero A., Lio P., Bengio Y. (2017). Graph attention networks. arXiv:1710.10903.
  199. Vinayakumar, R., Soman, K.P., Alazab, M., Sriram, S., Simran, K. (2020). A Comprehensive Tutorial and Survey of Applications of Deep Learning for Cyber Security. IEEE Communications Surveys & Tutorial, vol. 23, no. 3, pp. 1920-1955.
  200. Vrcek, L., Velickovic, P., Šikić, M. (2020). A step towards neural genome assembly. arXiv:2011.05013.
  201. Wagner, P., Marelli, S., Papaioannou, I., Starub, D., Sudret, B. (2022). Rare event estimation using stochastic spectral embedding. Structural safety, vol. 96, p. 102179.
  202. Wan, S., Gong, C., Zhong, P., Du, B., Zhang, L., Yang, J. (2019). Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. vol. 58, no. 5, pp. 3162-3177.
  203. Wan, C., Li, Y., Li, A., Kim, N.S., Lin, Y. (2022). BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling. Proceedings of Machine Learning and Systems, vol. 4, pp. 673-693.
  204. Wang, W., Daniels, T.E. (2008). A Graph Based Approach Toward Network Forensics Analysis. ACM Transactions on Information and System Security, vol. 12, no. 1, pp. 1-33.
  205. Wang, X., Ye, Y., Gupta, A. (2018). Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6857-6866.
  206. Wang, Y., He, D., Li, F., Long, X., Zhou, Z., Ma, J., Wen, S. (2019a). Multi-Label Classification with Label Graph Superimposing. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 7, pp. 12265-12272.
  207. Wang, S., Chen, Z., Ni, J., Yu, X., Li, Z., Chen, H., Yu, P.S. (2019b). Adversarial Defense Framework for Graph Neural Network. arXiv:1905.03679.
  208. Wang, D., Hu, D., Li, X., Dou, D. (2020a). Temporal Relational Modeling with Self-Supervision for Action Segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 4, pp. 2729-2737.
  209. Wang, R., Mao, J., Gershman, S.J., Wu, J. (2020b). Language-Mediated, Object-Centric Representation Learning. arXiv:2012.15814.
  210. Wang, Y., Min, Y., Chen, X., Wu, J. (2020c). Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction. In Proceedings of the Web Conference 2021, April, pp. 2921-2933.
  211. Wang, H., Leskovec, J. (2020d). Unifying Graph Convolutional Neural Networks and Label Propagation. arXiv:2002.06755.
  212. Wang, C., Liu, W., Li, H., Yu, S., Chen, S., Ye, X., Wu, J. (2021a). Dynamic Relevance Learning for Few-Shot Object Detection. IEEE Transactions on Neural Networks and Learning Systems. arXiv:2108.02235.
  213. Wang, Y., Wang, W., Liang, Y., Cai, Y., Hooi, B. (2021b). Mixup for Node and Graph Classification. In Proceedings of the Web Conference 2021, pp. 3663-3674.
  214. Wang, S., Wang, Z., Zhou, T., Yin, X., Han, D., Zhang, H., Sun, H., Shi, X., Yang, J. (2021c). ThreaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning. IEEE Transactions on Information Forensics and Security, vol. 17, pp. 3972-3987.
  215. Wang, F., Sun, K., Chen, M., Pujara, J., Szekely, P.A. (2021d). Retrieving Complex Tables with Multi-Granular Graph Representation Learning. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1472-1482.
  216. Wang, C., Shen, Y., Ji, L. (2022). Geometry Attention Transformer with position-aware LSTMs for image captioning. Expert Systems with Applications, vol. 201, p. 117174.
  217. Weber, M., Domeniconi, G., Chen, J., Weidele, D.K.I., Bellei, C., Robinson, T., Leiserson, C.E. (2019). Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics. In Proceedings of the 25th SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1-7.
  218. Wu, Y., Liu, X., Feng, Y., Wang, Z., Zhao, D. (2019a). Jointly Learning Entity and Relation Representations for Entity Alignment, arXiv:1909.09317.
  219. Wu, L., Li, S.,Hsieh, C., Sharpnack, J (2019b). Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers. In Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, Canada, pp. 1-11.
  220. Wu, L. (2020). Advances in Collaborative Filtering and Ranking. arXiv:2002.12312.
  221. Wu, Y., Dai, H.N., Tang, H. (2021). Graph Neural Networks for Anomaly Detection in Industrial Internet of Things. IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9214-9231.
  222. Wu, Y., Wang, J., Miao, X., Wang, W., Yin, J. (2024). Differentiable and Scalable Generative Adversarial Models for Data Imputation. IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 2, pp. 490-503.
  223. Xia, L., Huang, C., Xu, Y., Dai, P., Zhang, X., Yang, H., Pei, J., Bo, L. (2021). Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4486-4493.
  224. Xiao, H., Feng, J., Lin, G., Liu, Y., Zhang, M. (2018). MoNet: Deep Motion Exploitation for Video Object Segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1140-1148.
  225. Xu, H.C., Zhou, W.X. (2018). Modeling aggressive market order placements with Hawkes factor models. Plos One, vol. 15, no. 1, e0226667.
  226. Xu, K., Hu, W., Leskovec, J., Jegelka, S. (2019). How Powerful Are Graph Neural Networks?. arXiv:1810.00826.
  227. Yamamoto, T. (2019). Crystal Graph Neural Networks for Data Mining in Materials Science. Research Institute for Mathematical and Computational Sciences, LLC.
  228. Yamanaka, R. (2021). Graphs and Machine Learning for Cybersecurity. Graphs and Machine Learning for Cybersecurity, Medium.
  229. Yang, S., Lin, G., Jiang, Q., Lin, W. (2019). A Dilated Inception Network for Visual Saliency Prediction. IEEE Transactions on Multimedia, vol. 22, pp. 2163-2176.
  230. Yang, Y., Qui, J., Song, M., Tao, D.C., Wang, X. (2020). Distilling Knowledge from Graph Convolutional Networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7074-7083.
  231. Yang, C., Liu, J., Shi, C. (2021). Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework. In Proceedings of the Web Conference 2021, New York, NY, USA, pp. 1227-1237.
  232. Yang, K., Liu, D., Lei, W., Yang, B., Zhang, H., Zhao, X., Yao, W., Chen, B. (2022). GCPG: A General Framework for Controllable Paraphrase Generation. In Findings of the Association for Computational Linguistics: ACL 2022, pp. 4035-4047.
  233. Yao, L., Mao, C., Luo, Y. (2018). Graph Convolutional Networks for Text Classification. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 7370-7377.
  234. Yao, Y., Jafarian, Y., Park, H.S. (2019). MONET: Multiview Semi-supervised Keypoint Detection via Epipolar Divergence. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 753-762.
  235. Yao, P., Shen, S., Xu, M., Liu, P., Zhang, F., Xing, J., Shao, P., Kaffenberger, B., Xu, R.X. (2021). Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification. IEEE Transactions on Medical Imaging, vol. 41, no. 5, pp. 1242-1254.
  236. Yasunaga, M., Liang, P. (2020). Graph-based, Self-Supervised Program Repair from Diagnostic Feedback. In Proceedings of the International Conference on Machine Learning, pp. 10799-10808.
  237. Yasunaga, M., Ren, H., Bosselut, A., Liang, P., Leskovec, J. (2021). QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering. arXiv:2104.06378.
  238. Ye, J., Zhao, J., Ye, K., Xu, C.Z. (2020). Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction. In Proceedings of the 25th International Conference on Pattern Recognition, pp. 6702-6709. Yu, F., Jiang, X., Gong, Y., Zhao, S., Guo, X., Zheng, W.S., Zheng, F., Sun, X. (2020a). Devil’s in the Details: Aligning Visual Clues for Conditional Embedding in Person Re-Identification. arXiv:2009.05250.
  239. Yu, D., Yang, Y., Zhang, R., Wu, Y. (2020b). Knowledge Embedding Based Graph Convolutional Network. In Proceedings of the Web Conference 2021, April 19-23, Ljubljana, Slovenia, pp. 1619-1628.
  240. Yu, W., Qin, Z. (2020c). Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters. In Proceedings of the International Conference on Machine Learning, pp. 10936-10945.
  241. Yu, P., Zhao, Y., Li, C., Yuan, J., Chrn, C. (2020d). Structure-Aware Human-Action Generation. In Proceedings of the 16th European Conference on Computer Vision, Glasgow, UK, August 23-28, pp. 18-34.
  242. Yuan, Z., Zhang, W., Tian, C., Rong, X., Zhang, Z., Wang, H., fu, K., Sun, X. (2022). Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information. IEEE Transactions on Geoscience and Remote Sensing. vol. 60, p. 5620616, pp. 1-16.
  243. Yun, S., Jeong, M., Yoo, S., Lee, S., Yi, S.S., Kim, R., Kang, J., Kim, H.J. (2022). Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs. Neural Networks, vol. 153, pp. 104-119.
  244. Zang, X., Xie, Y., Chen, J., Yuan, B. (2020). Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models. arXiv:2002.04784.
  245. Zeng, H., Prasanna, V. (2020). GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms. In Proceedings of the 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, February pp. 255-265.
  246. Zeng, H., Zhang, M., Xia, Y., Srivastava, A., Malevichh, A., Kanan, R., Prasanna, V., Jin, L., Chen, R. (2021). Decoupling the Depth and Scope of Graph Neural Networks. Advances in Neural Information Processing Systems, vol. 34, pp. 19665-19679.
  247. Zhang, C., Loken, K., Chen, Z., Xiao, Z., Kunkel, G. (2018a). Mask Editor: an Image Annotation Tool for Image Segmentation Tasks. arXiv:1809.06461.
  248. Zhang, X.S., Chou, J., Wang, F. (2018b). Integrative Analysis of Patient Health Records and Neuroimages via Memory-based Graph Convolutional Network. In Proceedings of the 2018 IEEE International Conference on Data Mining, pp. 767-776.
  249. Zhang, C., Lyu, X., Tang, Z. (2019a). TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning. In Proceedings of the 27th ACM International Conference on Multimedia, pp. 1641-1649.
  250. Zhang, J. (2019b). Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview. arXiv:1908.00187.
  251. Zhang, J., Meng, L. (2019c). GResNet: Graph Residual Network for Reviving Deep GNNs from Suspended Animation. arXiv:1909.05729.
  252. Zhang, L., Liu, P., Choi, Y.H., Chen, P. (2020a). Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection. arXiv:2009.05602.
  253. Zhang, S., Liu, Y., Xie, L. (2020b). Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures. arXiv:2011.07457.
  254. Zhang, N., Francis, S., Malik, R., Chen, X. (2020c). A Spatially Constrained Deep Convolutional Neural Network for Nerve Fiber Segmentation in Corneal Confocal Microscopic Images using Inaccurate Annotations. In Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging, pp. 456-460.
  255. Zhang, Z., Jia, J., Wang, B., Gong, N.Z. (2021a). Backdoor Attacks to Graph Neural Networks. In Proceedings of the 26th ACM Symposium on Access Control Models and Technologies, June, pp. 15-26.
  256. Zhang, Y., Li, W., Zhang, M., Qu, Y., Tao R., Qi, H. (2021b). Topological Structure and Semantic Information Transfer Network for Cross-Scene Hyperspectral Image Classification. IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 6, pp. 2817-2830.
  257. Zhang, H., Gou, R., Shang, J., Shen, F., Wu, Y., Dai, G. (2021c). Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition. Frontiers in Physiology, vol. 12, p. 643202.
  258. Zhang, S., Chen, H., Yang, H., Sun, X., Yu, P.S., Xu, G. (2022). Graph Masked Autoencoders with Transformers. arXiv:2202.08391.
  259. Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Deng, M., Li, H. (2019). T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems. vol. 21, no. 9, pp. 3848-3858.
  260. Zhao, H., Wang, Y., Duan, J., Huang, C., Cao, D., Tong, Y., Xu, B., Bai, J., Tong, J., Zhang, Q. (2020). Multivariate Time-series Anomaly Detection via Graph Attention Network. In Proceedings of the 2020 IEEE International Conference on Data Mining, pp. 841-850.
  261. Zhao, J., Li, C., Wen, Q., Wang, Y., Liu, Y., Sun, H., Xie, X., Ye, X. (2021). Gophormer: Ego-Graph Transformer for Node Classification. arXiv:2110.13094.
  262. Zhaoa, P., Houb, L., Wua, O. (2020). Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification. Knowledge-Based Systems, vol. 193, p. 105443.
  263. Zhaou, Y., Ye, Q., Qui, Q., Jiao, J. (2017). Oriented Response Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 519-528.
  264. Zheng, Y., Gao, C., Chen, L., Jin, D., Li, Y. (2021). DGCN: Diversified Recommendation with Graph Convolutional Networks. In Proceedings of the Web Conference 2021, April, pp. 401-412.
  265. Zhou, Z., Zhang, S., Huang, Z. (2019). Dynamic Self-training Framework for Graph Convolutional Networks. arXiv:1910.02684.
  266. Zhou, X., Wang, H. (2020a). RoGAT: a robust GNN combined revised GAT with adjusted graphs. arXiv:2009.13038.
  267. Zhou, J., Xu, Z., Rush, A.M., Yu, M. (2020b). Automating Botnet Detection with Graph Neural Networks. arXiv:2003.06344.
  268. Zhu, H., Liu, X., Mao, X., Wong, T.T. (2017). Real-time Deep Video Deinterlacing. arXiv:1708.00187.
  269. Zhu, Q., Du, B., Yan, P. (2020a). Self-supervised Training of Graph Convolutional Networks. arXiv:2006.02380.
  270. Zhu, H., Feng, F., He, X., Wang, X., Li, Y., Zheng, K., Zhang, Y. (2020b). Bilinear Graph Neural Network with Neighbor Interactions. arXiv:2002.03575.
  271. Zhuo, J., Wang, S., Cui, S., Huang, Q. (2019). Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 750-759.
  272. Zou, Z., Tang, W. (2021). Modulated Graph Convolutional Network for 3D Human Pose Estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11477-11487.
  273. Zurel, M., Okay, C., Raussendorf, R., Heimendahl, A. (2021). Hidden Variable Model for Quantum Computation with Magic States on Any Number of Qudits of Any Dimension. arXiv:2110.12318.
  274. Bratholm, L.A., Gerrard, W., Anderson, B., Bai, S., Choi, S., Dang, L.,. Glowacki, D.R. (2021). A community-powered search of machine learning strategy space to find NMR property prediction models. PLOS ONE, vol. 16, no. 7, p. e0253612.
  275. Liang, B., Yin, R., Gui,L., Du, J., Xu, R. (2020). Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis. In Proceedings of the 28th International Conference on Computational Linguistics, pp. 150-161.