Deep Community Detection in Attributed Temporal Graphs: Experimental Evaluation of Current Approaches
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Abstract
Recent advances in network representation learning have sparked renewed interest in developing strategies for learning on spatio-temporal signals, crucial for applications like traffic forecasting, recommendation systems, and social network analysis. Despite the popularity of Graph Neural Networks for node-level clustering, most specialized solutions are evaluated in transductive learning settings, where the entire graph is available during training, leaving a significant gap in understanding their performance in inductive learning settings. This work presents an experimental evaluation of community detection approaches on temporal graphs, comparing traditional methods with deep learning models geared toward node-level clustering. We assess their performance on six real-world datasets, focused on a transductive setting and extending to an inductive setting for one dataset. Our results show that deep learning models for graphs do not consistently outperform more established methods on this task, highlighting the need for more effective approaches and comprehensive benchmarks for their evaluation.
References
[1]
Shaked Brody, Uri Alon, and Eran Yahav. 2022. How Attentive are Graph Attention Networksarxiv: 2105.14491 [cs.LG]
[2]
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation. In 2014 Conference on Empirical Methods in Natural Language Processing EMNLP. Association for Computational Linguistics, 1724--1734. https://doi.org/10.3115/v1/D14--1179
[3]
Nelson Aloysio Reis de Almeida Passos, Emanuele Carlini, and Salvatore Trani. 2024.-Temporal: a dynamic graph dataset with node-level features. https://doi.org/10.5281/zenodo.13932076
[4]
Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.
[5]
Santo Fortunato. 2010. Community Detection in Graphs. Physics Reports, Vol. 486, 3--5 (2 2010), 75--174. https://doi.org/10.1016/j.physrep.2009.11.002
[6]
F.A. Gers, J. Schmidhuber, and F. Cummins. 1999. Learning to forget: continual prediction with LSTM. In Ninth International Conference on Artificial Neural Networks ICANN, Vol. 2. 850--855. https://doi.org/10.1049/cp:19991218
[7]
Amir Ghasemian, Pan Zhang, Aaron Clauset, Cristopher Moore, and Leto Peel. 2016. Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks. Physical Review X, Vol. 6, 3 (July 2016). https://doi.org/10.1103/physrevx.6.031005
[8]
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural Message Passing for Quantum Chemistry. In 34th International Conference on Machine Learning - Volume 70 (Sydney, NSW, Australia) (ICML'17). JMLR.org, 1263--1272.
[9]
Daniele Grattarola, Daniele Zambon, Filippo Maria Bianchi, and Cesare Alippi. 2022. Understanding Pooling in Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems (2022), 1--11. https://doi.org/10.1109/TNNLS.2022.3190922
[10]
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In 22nd ACM SIGKDD. ACM. https://doi.org/10.1145/2939672.2939754
[11]
Jin Guo, Zhen Han, Su Zhou, Jiliang Li, Volker Tresp, and Yuyi Wang. 2022. Continuous Temporal Graph Networks for Event-Based Graph Data. In DLG4NLP 2022. Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.dlg4nlp-1.3
[12]
Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna R Narayanan, Mingyuan Zhou, and Xiaoning Qian. 2020. Variational Graph Recurrent Neural Networks. arxiv: 1908.09710 [cs.LG]
[13]
Bronwyn Hall, Adam Jaffe, and Manuel Trajtenberg. 2001. The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools. https://doi.org/10.3386/w8498
[14]
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc.
[15]
Alan G. Hawkes. 1971. Point Spectra of Some Mutually Exciting Point Processes. Journal of the Royal Statistical Society Series B: Statistical Methodology, Vol. 33, 3 (Oct. 1971), 438--443. https://doi.org/10.1111/j.2517--6161.1971.tb01530.x
[16]
Thomas N Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. NIPS Workshop on Bayesian Deep Learning (2016).
[17]
Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR. arxiv: 1609.02907 [cs.LG]
[18]
Florent Krzakala, Cristopher Moore, Elchanan Mossel, Joe Neeman, Allan Sly, Lenka Zdeborová, and Pan Zhang. 2013. Spectral redemption in clustering sparse networks. Proceedings of the National Academy of Sciences, Vol. 110, 52 (Nov. 2013), 20935--20940. https://doi.org/10.1073/pnas.1312486110
[19]
Pan Li and Jure Leskovec. 2022. Graph Neural Networks: Foundations, Frontiers, and Applications. Springer Singapore, Singapore.
[20]
Meng Liu, Ke Liang, Yue Liu, Siwei Wang, Sihang Zhou, and Xinwang Liu. 2023. arXiv4TGC: Large-Scale Datasets for Temporal Graph Clustering. arxiv: 2306.04962 [cs.AI] https://arxiv.org/abs/2306.04962
[21]
Meng Liu, Yue Liu, Ke Liang, Wenxuan Tu, Siwei Wang, Sihang Zhou, and Xinwang Liu. 2024. Deep Temporal Graph Clustering. In The 12th International Conference on Learning Representations.
[22]
Antonio Longa, Veronica Lachi, Gabriele Santin, Monica Bianchini, Bruno Lepri, Pietro Lio, Franco Scarselli, and Andrea Passerini. 2023. Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities. Transactions on Machine Learning Research (2023).
[23]
Sedigheh Mahdavi, Shima Khoshraftar, and Aijun An. 2018. dynnode2vec: Scalable Dynamic Network Embedding. In 2018 IEEE International Conference on Big Data (Big Data). IEEE. https://doi.org/10.1109/bigdata.2018.8621910
[24]
Rossana Mastrandrea, Julie Fournet, and Alain Barrat. 2015. Contact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship Surveys. PLOS ONE, Vol. 10, 9 (Sept. 2015), e0136497. https://doi.org/10.1371/journal.pone.0136497
[25]
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. arxiv: 1301.3781 [cs.CL]
[26]
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe. 2019. Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks. In Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence (Honolulu, Hawaii, USA) (AAAI'19/IAAI'19/EAAI'19). AAAI Press, Article 565, 8 pages. https://doi.org/10.1609/aaai.v33i01.33014602
[27]
Galileo Mark Namata, Ben London, Lise Getoor, Bert Huang, and U Edu. 2012. Query-driven active surveying for collective classification. In 10th International Workshop on Mining and Learning with Graphs, Vol. 8. MLG.
[28]
Mark Newman. 2018. Networks 2 ed.). Oxford University Press.
[29]
Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B. Schardl, and Charles E. Leiserson. 2019. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. arxiv: 1902.10191 [cs.LG]
[30]
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, Vol. 12 (2011), 2825--2830.
[31]
Tiago P. Peixoto. 2019. Bayesian Stochastic Blockmodeling., 289--332 pages. https://doi.org/10.1002/9781119483298.ch11
[32]
Maria Giulia Preti, Thomas AW Bolton, and Dimitri Van De Ville. 2017. The dynamic functional connectome: State-of-the-art and perspectives. NeuroImage, Vol. 160 (Oct. 2017), 41--54. https://doi.org/10.1016/j.neuroimage.2016.12.061
[33]
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020. Temporal Graph Networks for Deep Learning on Dynamic Graphs. arxiv: 2006.10637 [cs.LG]
[34]
Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang. 2020. DySAT. In 13th International Conference on Web Search and Data Mining. ACM. https://doi.org/10.1145/3336191.3371845
[35]
Uriel Singer, Ido Guy, and Kira Radinsky. 2019. Node embedding over temporal graphs. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (Macao, China) (IJCAI'19). AAAI Press, 4605--4612.
[36]
V. A. Traag, L. Waltman, and N. J. van Eck. 2019. From Louvain to Leiden: guaranteeing well-connected community. Scientific Reports, Vol. 9, 1 (26 3 2019), 5233. https://doi.org/10.1038/s41598-019--41695-z
[37]
Anton Tsitsulin, John Palowitch, Bryan Perozzi, and Emmanuel Müller. 2024. Graph Clustering with Graph Neural Networks. J. Mach. Learn. Res., Vol. 24, 1, Article 127 (mar 2024), 21 pages.
[38]
Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. arxiv: 1710.10903 [stat.ML]
[39]
Nguyen Xuan Vinh, Julien Epps, and James Bailey. 2009. Information theoretic measures for clusterings comparison: is a correction for chance necessary?. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09). ACM. https://doi.org/10.1145/1553374.1553511
[40]
Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Attributed Graph Clustering: A Deep Attentional Embedding Approach. In Twenty-Eighth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2019/509
[41]
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2021. A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, 1 (2021), 4--24. https://doi.org/10.1109/TNNLS.2020.2978386
[42]
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive Representation Learning on Temporal Graphs. arxiv: 2002.07962 [cs.LG]
[43]
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networksarxiv: 1810.00826 [cs.LG]
[44]
Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. 2016. Revisiting semi-supervised learning with graph embeddings. In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 (New York, NY, USA) (ICML'16). JMLR.org, 40--48.
[45]
Yuan Zuo, Guannan Liu, Hao Lin, Jia Guo, Xiaoqian Hu, and Junjie Wu. 2018. Embedding Temporal Network via Neighborhood Formation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). ACM. https://doi.org/10.1145/3219819.3220054
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58 pages
ISBN:9798400712548
DOI:10.1145/3694811
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Published: 09 December 2024
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