Multi-View Collaborative Network Embedding

TL;DR

This paper introduces MANE, a multi-view network embedding method that captures view diversity and collaboration, including a novel second-order collaboration, and extends it with MANE+ using attention to model node-specific view importance, achieving superior results.

Contribution

The paper proposes a novel multi-view network embedding framework that incorporates second-order collaboration and an attention mechanism for node-specific view importance, outperforming existing methods.

Findings

MANE outperforms state-of-the-art methods on real-world datasets.

Second-order collaboration enhances node representation quality.

Attention-based MANE+ effectively models node-wise view importance.

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, 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 paper, we propose 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.