Publication Type

Journal Article

Version

publishedVersion

Publication Date

6-2021

Abstract

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked, if they have common favorite videos in one view, then they can also be linked in another view if they share common subscribers. Unlike traditional single-view networks, multiple views maintain different semantics to complement each other. In this article, we propose Multi-view collAborative Network Embedding (MANE), a multi-view network embedding approach to learn low-dimensional representations. Similar to existing studies, MANE hinges on diversity and collaboration—while diversity enables views to maintain their individual semantics, collaboration enables views to work together. However, we also discover a novel form of second-order collaboration that has not been explored previously, and further unify it into our framework to attain superior node representations. Furthermore, as each view often has varying importance w.r.t. different nodes, we propose MANE, an attention-based extension of MANE, to model node-wise view importance. Finally, we conduct comprehensive experiments on three public, real-world multi-view networks, and the results demonstrate that our models consistently outperform state-of-the-art approaches.

Keywords

multi-view networks, network embedding

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Knowledge Discovery from Data

Volume

15

Issue

3

First Page

1

Last Page

18

ISSN

1556-4681

Identifier

10.1145/3441450

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3441450

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