Abstract
Graph representation learning, which maps high-dimensional graphs or sparse graphs into a low-dimensional vector space, has shown its superiority in numerous learning tasks. Recently, researchers have identified some advantages of context-sensitive graph representation learning methods in functions such as link predictions and ranking recommendations. However, most existing methods depend on convolutional neural networks or recursive neural networks to obtain additional information outside a node, or require community algorithms to extract multiple contexts of a node, or focus only on the local neighboring nodes without their structural information. In this paper, we propose a novel context-sensitive representation method, Context-Sensitive Graph Representation Learning (CSGRL), which simultaneously combines attention networks and a variant of graph auto-encoder to learn weighty information about various aspects of participating neighboring nodes. The core of CSGRL is to utilize an asymmetric graph encoder to aggregate information about neighboring nodes and local structures to optimize the learning goal. The main benefit of CSGRL is that it does not need additional features and multiple contexts for the node. The message of neighboring nodes and their structures spread through the encoder. Experiments are conducted on three real datasets for both tasks of link prediction and node clustering, and the results demonstrate that CSGRL can significantly improve the effectiveness of all challenging learning tasks compared with 14 state-of-the-art baselines.
Similar content being viewed by others
References
Abu-El-Haija S, Perozzi B, Al-Rfou R et al. (2017) Watch your step: learning graph embeddings through attention. arXiv preprint arXiv: 1710.09599
Caron M, Bojanowski P, Joulin A et al (2018) Deep clustering for unsupervised learning of visual features. In: Proceedings of the European Conference on Computer Vision (ECCV). pp: 132–149
Chen L, Guan Z, Xu Q et al (2020) Question-driven purchasing propensity analysis for recommendation. Proc AAAI Conf Artif Intell 34(01): 35–42
Epasto A, Perozzi B (2019) Is a single embedding enough? Learning node representations that capture multiple social contexts. In: The World Wide Web Conference, pp 394–404
Gracious T, Dukkipati A (2020) Adversarial context aware network embeddings for textual networks. arXiv preprint arXiv:2011.02665
Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 855–864
Grover A, Zweig A, Ermon S (2019) Graphite: iterative generative modeling of graphs. In: International conference on machine learning. PMLR, 2434–2444
Haghani S, Keyvanpour MR (2019) A systemic analysis of link prediction in social network. Artif Intell Rev 52(3):1961–1995
Hasanzadeh A, Hajiramezanali E, Duffield N et al. (2019) Semi-implicit graph variational auto-encoders. arXiv preprint arXiv:1908.07078
Huang PY, Frederking R (2019) Rwr-gae: random walk regularization for graph auto encoders. arXiv preprint arXiv:1908.04003
Kefato ZT, Girdzijauskas S (2020) Graph neighborhood attentive pooling. arXiv preprint arXiv: 2001.10394
Kefato Z, Girdzijauskas S (2020) Gossip and attend: context-sensitive graph representation learning. In: Proceedings of the International AAAI Conference on Web and Social Media, pp: 351–359
Kefato ZT, Sheikh N, Montresor A (2017) Mineral: multi-modal network representation learning. In: International Workshop on Machine Learning, Optimization, and Big Data, pp 286–298
Kingma DP, Welling M (2013) Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114
Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv preprint arXiv: 1611.07308
Kipf TN, Welling M (2017) Semi-supervised Classification with Graph Convolutional Networks. arXiv preprint arXiv: 1609.02907
Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov Data (TKDD) 1(1):2–es
Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inform Sci Technol 58(7):1019–1031
Pan S, Wu J, Zhu X et al (2016) Tri-party deep network representation. Network 11(9):12
Pan S, Hu R, Long G et al. (2018) Adversarially regularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407
Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 701–710
Perozzi B, Kulkarni V, Skiena S (2016) Walklets: Multiscale graph embeddings for interpretable network classification. arXiv preprint arXiv: 1605.02115
Qin J, Zeng X, Wu S et al (2020) E-GCN: graph convolution with estimated labels. Appl Intell 51(7):5007–5015
Sheikh N, Kefato Z, Montresor A (2019) gat2vec: representation learning for attributed graphs. Computing 101(3):187–209
Sun X, Guo J, Ding X, et al. (2016) A general framework for content-enhanced network representation learning. arXiv preprint arXiv: 1610.02906
Tang J, Qu M, Wang M, et al. (2015) Line: large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web, pp 1067–1077
Tu C, Liu H, Liu Z, et al. (2017) Cane: Context-aware network embedding for relation modeling. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 1722–1731
Vinh NX, Epps J, Bailey J (2010) Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J Mach Learn Res 11:2837–2854
Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1225–1234
Wang W, Tao C, Gan Z et al (2019) Improving textual network learning with variational homophilic embeddings. arXiv preprint arXiv:1909.13456
Xie J, Girshick R, Farhadi A (2016) Unsupervised deep embedding for clustering analysis. In: International conference on machine learning. PLMR. pp 478–487
Yang C, Liu Z, Zhao D et al (2015) Network representation learning with rich text information. In: IJCAI, pp 2111–2117
Yang C, Pal A, Zhai A, et al. (2020) MultiSage: Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 2434–2443
Yu X, Ren X, Sun Y et al (2014) Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th ACM international conference on Web search and data mining, pp 283–292
Zhang X, Li Y, Shen D et al (2018) Diffusion maps for textual network embedding. arXiv preprint arXiv:1805.09906
Acknowledgements
This work was supported by the Postgraduate Research and Practice Innovation Program of Jiangsu Province.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Qin, J., Zeng, X., Wu, S. et al. Context-sensitive graph representation learning. Int. J. Mach. Learn. & Cyber. 14, 2193–2203 (2023). https://doi.org/10.1007/s13042-022-01755-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-022-01755-9