Abstract
Graph structure data has seen widespread utilization in modeling and learning representations, with dynamic graph neural networks being a popular choice. However, existing approaches to dynamic representation learning suffer from either discrete learning, leading to the loss of temporal information, or continuous learning, which entails significant computational burdens. Regarding these issues, we propose an innovative dynamic graph neural network called Event Sparse Net (ESN). By encoding time information adaptively as snapshots and there is an identical amount of temporal structure in each snapshot, our approach achieves continuous and precise time encoding while avoiding potential information loss in snapshot-based methods. Additionally, we introduce a lightweight module, namely Global Temporal Attention, for computing node representations based on temporal dynamics and structural neighborhoods. By simplifying the fully-connected attention fusion, our approach significantly reduces computational costs compared to the currently best-performing methods. We assess our methodology on four continuous/discrete graph datasets for link prediction to assess its effectiveness. In comparison experiments with top-notch baseline models, ESN achieves competitive performance with faster inference speed.
Supported by the National Natural Science Foundation of China under Grant 62002074 and 62072452; Supported by the Shenzhen Science and Technology Program JCYJ20200109115627045, in part by the Regional Joint Fund of Guangdong under Grant 2021B1515120011.
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References
Zhu, T., Li, J., Hu, X., Xiong, P., Zhou, W.: The dynamic privacy-preserving mechanisms for online dynamic social networks. IEEE Trans. Knowl. Data Eng. 34(06), 2962–2974 (2022)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)
Goering, S., Klein, E.: Fostering neuroethics integration with neuroscience in the brain initiative: comments on the nih neuroethics roadmap. AJOB Neurosci. 11(3), 184–188 (2020)
Fout, A.M.: Protein interface prediction using graph convolutional networks. Ph.D. dissertation, Colorado State University (2017)
Jiang, L., Cheng, Y., Yang, L., Li, J., Yan, H., Wang, X.: A trust-based collaborative filtering algorithm for e-commerce recommendation system. J. Ambient. Intell. Humaniz. Comput. 10(8), 3023–3034 (2019)
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974–983 (2018)
Chen, C., Huang, T.: Camdar-adv: generating adversarial patches on 3d object. Int. J. Intell. Syst. 36(3), 1441–1453 (2021)
Shi, Z., Chang, C., Chen, H., Du, X., Zhang, H.: PR-NET: progressively-refined neural network for image manipulation localization. Int. J. Intell. Syst. 37(5), 3166–3188 (2022)
Yan, H., Chen, M., Hu, L., Jia, C.: Secure video retrieval using image query on an untrusted cloud. Appl. Soft Comput. 97, 106782 (2020)
Ai, S., Koe, A.S.V., Huang, T.: Adversarial perturbation in remote sensing image recognition. Appl. Soft Comput. 105, 107252 (2021)
Wang, X., Li, J., Li, J., Yan, H.: Multilevel similarity model for high-resolution remote sensing image registration. Inf. Sci. 505, 294–305 (2019)
Wang, X., Li, J., Yan, H.: An improved anti-quantum mst3 public key encryption scheme for remote sensing images. Enterp. Inf. Syst. 15(4), 530–544 (2021)
Li, J., et al.: Efficient and secure outsourcing of differentially private data publishing with multiple evaluators. IEEE Trans. Depend. Secure Comput. 19(01), 67–76 (2022)
Yan, H., Hu, L., Xiang, X., Liu, Z., Yuan, X.: PPCL: privacy-preserving collaborative learning for mitigating indirect information leakage. Inf. Sci. 548, 423–437 (2021)
Barros, C.D., Mendonça, M.R., Vieira, A.B., Ziviani, A.: A survey on embedding dynamic graphs. ACM Comput. Surv. (CSUR) 55(1), 1–37 (2021)
Cai, H., Zheng, V.W., Chang, K.C.-C.: A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30(9), 1616–1637 (2018)
Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31(5), 833–852 (2018)
Kazemi, S.M., et al.: Representation learning for dynamic graphs: a survey. J. Mach. Learn. Res. 21(70), 1–73 (2020)
Skarding, J., Gabrys, B., Musial, K.: "Foundations and modeling of dynamic networks using dynamic graph neural networks: a survey. IEEE Access 9, 79143–79168 (2021)
Pang, Y., et al.: SPARSE-DYN: sparse dynamic graph multirepresentation learning via event-based sparse temporal attention network. Int. J. Intell. Syst. 37(11), 8770–8789 (2022)
Chen, J., Xu, X., Wu, Y., Zheng, H.: GC-LSTM: graph convolution embedded LSTM for dynamic link prediction. arXiv preprint arXiv:1812.04206 (2018)
Goyal, P., Chhetri, S.R., Canedo, A.: dyngraph2vec: capturing network dynamics using dynamic graph representation learning. Knowl.-Based Syst. 187, 104816 (2020)
Pang, Y., et al.: Graph decipher: a transparent dual-attention graph neural network to understand the message-passing mechanism for the node classification. Int. J. Intell. Syst. 37(11), 8747–8769 (2022)
Jiang, N., Jie, W., Li, J., Liu, X., Jin, D.: GATRUST: a multi-aspect graph attention network model for trust assessment in OSNS. IEEE Trans. Knowl. Data Eng. 01, 1–1 (2022)
Kumar, S., Zhang, X., Leskovec, J.: Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1269–1278 (2019)
Ma, Y., Guo, Z., Ren, Z., Tang, J., Yin, D.: Streaming graph neural networks. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 719–728 (2020)
Sankar, A., Wu, Y., Gou, L., Zhang, W., Yang, H.: DYSAT: deep neural representation learning on dynamic graphs via self-attention networks. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 519–527 (2020)
Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., Achan, K.: Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962 (2020)
Zhou, L., Yang, Y., Ren, X., Wu, F., Zhuang, Y.: Dynamic network embedding by modeling triadic closure process. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)
Chen, X., Zhang, F., Zhou, F., Bonsangue, M.: Multi-scale graph capsule with influence attention for information cascades prediction. Int. J. Intell. Syst. 37(3), 2584–2611 (2022)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017)
Guo, Q., Qiu, X., Liu, P., Shao, Y., Xue, X., Zhang, Z.: Star-transformer. arXiv preprint arXiv:1902.09113 (2019)
Latapy, M., Viard, T., Magnien, C.: Stream graphs and link streams for the modeling of interactions over time. Soc. Netw. Anal. Min. 8(1), 1–29 (2018)
Taheri, A., Gimpel, K., Berger-Wolf, T.: Learning to represent the evolution of dynamic graphs with recurrent models. In: Companion Proceedings of The 2019 World Wide Web Conference, pp. 301–307 (2019)
Hajiramezanali, E., Hasanzadeh, A., Duffield, N., Narayanan, K.R., Zhou, M., Qian, X.: Variational graph recurrent neural networks. arXiv preprint arXiv:1908.09710 (2019)
Klimt, B., Yang, Y.: The enron corpus: a new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_22
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256. JMLR Workshop and Conference Proceedings (2010)
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Li, D. et al. (2024). Event Sparse Net: Sparse Dynamic Graph Multi-representation Learning with Temporal Attention for Event-Based Data. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14433. Springer, Singapore. https://doi.org/10.1007/978-981-99-8546-3_17
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DOI: https://doi.org/10.1007/978-981-99-8546-3_17
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