research-article

struc2vec: Learning Node Representations from Structural Identity

Authors:

Leonardo F.R. Ribeiro,

Pedro H.P. Saverese,

Daniel R. FigueiredoAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 385 - 394

https://doi.org/10.1145/3097983.3098061

Published: 04 August 2017 Publication History

Abstract

Structural identity is a concept of symmetry in which network nodes are identified according to the network structure and their relationship to other nodes. Structural identity has been studied in theory and practice over the past decades, but only recently has it been addressed with representational learning techniques. This work presents struc2vec, a novel and flexible framework for learning latent representations for the structural identity of nodes. struc2vec uses a hierarchy to measure node similarity at different scales, and constructs a multilayer graph to encode structural similarities and generate structural context for nodes. Numerical experiments indicate that state-of-the-art techniques for learning node representations fail in capturing stronger notions of structural identity, while struc2vec exhibits much superior performance in this task, as it overcomes limitations of prior approaches. As a consequence, numerical experiments indicate that struc2vec improves performance on classification tasks that depend more on structural identity.

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References

[1]

Nir Atias and Roded Sharan. 2012. Comparative analysis of protein networks: hard problems, practical solutions. Commun. ACM 55 (2012).

Digital Library

[2]

Yoshua Bengio, Rejean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A Neural Probabilistic Language Model. JMLR (2003).

Digital Library

[3]

V Blondel, A Gajardo, M Heymans, P Senellart, and P Van Dooren. 2004. A measure of similarity between graph vertices: Applications to synonym extraction and web searching. SIAM review (2004).

[4]

Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2016. Deep Neural Networks for Learning Graph Representations. In AAAI.

[5]

F Fouss, A Pirotte, J Renders, and M Saerens. 2007. Random-Walk Computation of Similarities Between Nodes of a Graph with Application to Collaborative Recommendation. IEEE Trans. on Knowl. and Data Eng. (2007).

Digital Library

[6]

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In ACM SIGKDD.

[7]

K Henderson, B Gallagher, T Eliassi-Rad, H Tong, S Basu, L Akoglu, D Koutra, C Faloutsos, and L Li. 2012. Rolx: structural role extraction & mining in large graphs. In ACM SIGKDD.

Digital Library

[8]

Jon M Kleinberg. 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM)(1999).

[9]

Elizabeth A Leicht, Petter Holme, and Mark EJ Newman. 2006. Vertex similarity in networks. Physical Review E 73 (2006).

[10]

Jure Leskovec and Julian J Mcauley. 2012. Learning to discover social circles in ego networks. In NIPS.

[11]

Francois Lorrain and Harrison C White. 1971. Structural equivalence of individuals in social networks. The Journal of mathematical sociology 1 (1971).

[12]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In ICLR Workshop.

[13]

T Mikolov, I Sutskever, K Chen, G Corrado, and J Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In NIPS.

[14]

A Narayanan, M Chandramohan, L Chen, Y Liu, and S Saminathan. 2016. subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs. In Workshop on Mining and Learning with Graphs.

[15]

Pedram Pedarsani and Matthias Grossglauser. 2011. On the privacy of anonymized networks. In ACM SIGKDD.

[16]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online Learning of Social Representations. In ACM SIGKDD.

[17]

Narciso Pizarro. 2007. Structural Identity and Equivalence of Individuals in Social Networks Beyond Duality. International Sociology 22 (2007).

[18]

T Rakthanmanon, B Campana, A Mueen, G Batista, B Westover, Q Zhu, J Zakaria, and E Keogh. 2013. Addressing big data time series: Mining trillions of time series subsequences under dynamic time warping. ACM TKDD (2013).

[19]

Lee Douglas Sailer. 1978. Structural equivalence: Meaning and definition, computation and application. Social Networks (1978).

[20]

S Salvador and P Chan. 2004. FastDTW: Toward accurate dynamic time warping in linear time and space. In Workshop on Min. Temp. and Seq. Data, ACM SIGKDD.

[21]

N Shervashidze, P Schweitzer, E van Leeuwen, K Mehlhorn, and K Borgwardt. 2011. Weisfeiler-Lehman Graph Kernels. JMLR (2011).

[22]

R Singh, J Xu, and B Berger. 2008. Global alignment of multiple protein interaction networks with application to functional orthology detection. PNAS(2008).

[23]

Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale Information Network Embedding. In WWW.

Digital Library

[24]

Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural Deep Network Embedding. In ACM SIGKDD.

Digital Library

[25]

Wayne W Zachary. 1977. An information flow model for conflict and fission in small groups. Journal of anthropological research (1977).

[26]

Laura A Zager and George C Verghese. 2008. Graph similarity scoring and matching. Applied mathematics letters (2008).

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Index Terms

struc2vec: Learning Node Representations from Structural Identity
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
    2. Machine learning approaches
      1. Learning latent representations

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cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 ACM.

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Published: 04 August 2017

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August 13 - 17, 2017

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Ahmad WWang B(2025)A learning-based influence maximization framework for complex networks via K-core hierarchies and reinforcement learningExpert Systems with Applications10.1016/j.eswa.2024.125393259(125393)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125393
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