Link prediction via subgraph embedding-based convex matrix completion

Zhu Cao, Linlin Wang, Gerard De Melo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Link prediction is of fundamental importance in network science and machine learning. Early methods consider only simple topological features, while subsequent supervised approaches typically rely on human-labeled data and feature engineering. In this work, we present a new representation learning-based approach called SEMAC that jointly exploits fine-grained node features as well as the overall graph topology. In contrast to the SGNS or SVD methods espoused in previous representation-based studies, our model represents nodes in terms of subgraph embeddings acquired via a form of convex matrix completion to iteratively reduce the rank, and thereby, more effectively eliminate noise in the representation. Thus, subgraph embeddings and convex matrix completion are elegantly integrated into a novel link prediction framework. Experimental results on several datasets show the effectiveness of our method compared to previous work.

Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Pages2803-2810
Number of pages8
ISBN (Electronic)9781577358008
StatePublished - Jan 1 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 7 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Other

Other32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period2/2/182/7/18

Fingerprint

Singular value decomposition
Learning systems
Topology

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Cao, Z., Wang, L., & De Melo, G. (2018). Link prediction via subgraph embedding-based convex matrix completion. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 2803-2810). (32nd AAAI Conference on Artificial Intelligence, AAAI 2018). AAAI press.
Cao, Zhu ; Wang, Linlin ; De Melo, Gerard. / Link prediction via subgraph embedding-based convex matrix completion. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. pp. 2803-2810 (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).
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Cao, Z, Wang, L & De Melo, G 2018, Link prediction via subgraph embedding-based convex matrix completion. in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, AAAI press, pp. 2803-2810, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, New Orleans, United States, 2/2/18.

Link prediction via subgraph embedding-based convex matrix completion. / Cao, Zhu; Wang, Linlin; De Melo, Gerard.

32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. p. 2803-2810 (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Cao Z, Wang L, De Melo G. Link prediction via subgraph embedding-based convex matrix completion. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press. 2018. p. 2803-2810. (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).