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Hate Speech Detection on Social Media Using Graph Convolutional Networks Read chapter Read first chapter

2022 | OriginalPaper | Chapter

Joint Use of Node Attributes and Proximity for Node Classification

Authors : Arpit Merchant, Michael Mathioudakis

Published in: Complex Networks & Their Applications X

Publisher: Springer International Publishing

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Abstract

Node classification aims to infer unknown node labels from known labels and other node attributes. Standard approaches for this task assume homophily, whereby a node’s label is predicted from the labels of other nodes nearby in the network. However, there are also cases of networks where labels are better predicted from the individual attributes of each node rather than the labels of nearby nodes. Ideally, node classification methods should flexibly adapt to a range of settings wherein unknown labels are predicted either from labels of nearby nodes, or individual node attributes, or partly both. In this paper, we propose a principled approach, JANE, based on a generative probabilistic model that jointly weighs the role of attributes and node proximity via embeddings in predicting labels. Experiments on multiple network datasets demonstrate that JANE exhibits the desired combination of versatility and competitive performance compared to baselines.

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previous chapter Supervised Link Weight Prediction Using Node Metadata
next chapter Online Updates of Knowledge Graph Embedding
Footnotes
Literature
1.
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006)MathSciNetMATH
2.
Blum, A., Lafferty, J., Rwebangira, M.R., Reddy, R.: Semi-supervised learning using randomized mincuts. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 13. ACM (2004)
3.
Cong, W., Forsati, R., Kandemir, M., Mahdavi, M.: Minimal variance sampling with provable guarantees for fast training of graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1393–1403 (2020)
4.
Gao, H., Huang, H.: Deep attributed network embedding. In: IJCAI (2018)
5.
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)
6.
7.
Huang, X., Li, J., Hu, X.: Accelerated attributed network embedding. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 633–641. SIAM (2017)
8.
Huang, X., Li, J., Hu, X.: Label informed attributed network embedding. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 731–739 (2017)
9.
Joachims, T.: Transductive learning via spectral graph partitioning. In: Proceedings of the 20th International Conference on Machine Learning (ICML-2003), pp. 290–297 (2003)
10.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (ICLR) (2017)
11.
Li, J., Hu, X., Tang, J., Liu, H.: Unsupervised streaming feature selection in social media. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (2015)
12.
Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
13.
Lorrain, F., White, H.C.: Structural equivalence of individuals in social networks. J. Math. Soc. 1(1), 49–80 (1971)CrossRef
14.
Marsden, P.V., Friedkin, N.E.: Network studies of social influence. Sociol. Methods Res. 22(1), 127–151 (1993)CrossRef
15.
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Soc. 27(1), 415–444 (2001)CrossRef
16.
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH
17.
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
18.
Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., Tang, J.: Network embedding as matrix factorization: unifying deepwalk, Line, PTE, and node2vec. In: Proceedings of the eleventh ACM International Conference on Web Search and Data Mining, pp. 459–467 (2018)
19.
Ribeiro, L.F., Saverese, P.H., Figueiredo, D.R.: struc2vec: learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 385–394. ACM (2017)
20.
Sailer, L.D.: Structural equivalence: meaning and definition, computation and application. Soc. Netw. 1(1), 73–90 (1978)CrossRef
21.
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93–93 (2008)
22.
Subramanian, A., et al.: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. 102(43), 15545–15550 (2005)CrossRef
23.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:​1710.​10903 (2017)
24.
25.
Wang, X., Zhu, M., Bo, D., Cui, P., Shi, C., Pei, J.: AM-GCN: adaptive multi-channel graph convolutional networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1243–1253 (2020)
26.
Wu, J., He, J., Xu, J.: Net: Degree-specific graph neural networks for node and graph classification. arXiv preprint arXiv:​1906.​02319 (2019)
27.
Yang, Z., Cohen, W., Salakhudinov, R.: Revisiting semi-supervised learning with graph embeddings. In: International Conference on Machine Learning, pp. 40–48. PMLR (2016)
28.
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: NeurIPS, pp. 4800–4810 (2018)
29.
30.
Zhu, X., Goldberg, A.B.: Introduction to semi-supervised learning. Synth. Lect. Artif. Intell. Mach. Learn. 3(1), 1–130 (2009)MATH
Metadata
Title
Joint Use of Node Attributes and Proximity for Node Classification
Authors
Arpit Merchant
Michael Mathioudakis
Copyright Year
2022
Book
Complex Networks & Their Applications X

Print ISBN: 978-3-030-93412-5

Electronic ISBN: 978-3-030-93413-2

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-030-93413-2_43

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