mvn2vec: Preservation and Collaboration in Multi-View Network Embedding

TL;DR

This paper introduces mvn2vec, a novel multi-view network embedding method that explicitly models preservation and collaboration objectives, demonstrating improved embedding quality through experiments on synthetic and real datasets.

Contribution

It proposes a new multi-view network embedding approach that simultaneously optimizes preservation and collaboration objectives, enhancing embedding quality without added complexity.

Findings

Modeling both preservation and collaboration improves embedding quality.

Experiments on synthetic and real datasets validate the approach.

The method does not require additional supervision or complexity.

Abstract

Multi-view networks are broadly present in real-world applications. In the meantime, network embedding has emerged as an effective representation learning approach for networked data. Therefore, we are motivated to study the problem of multi-view network embedding with a focus on the optimization objectives that are specific and important in embedding this type of network. In our practice of embedding real-world multi-view networks, we explicitly identify two such objectives, which we refer to as preservation and collaboration. The in-depth analysis of these two objectives is discussed throughout this paper. In addition, the mvn2vec algorithms are proposed to (i) study how varied extent of preservation and collaboration can impact embedding learning and (ii) explore the feasibility of achieving better embedding quality by modeling them simultaneously. With experiments on a series of synthetic datasets, a large-scale internal Snapchat dataset, and two public datasets, we confirm the validity and importance of preservation and collaboration as two objectives for multi-view network embedding. These experiments further demonstrate that better embedding can be obtained by simultaneously modeling the two objectives, while not over-complicating the model or requiring additional supervision. The code and the processed datasets are available at http://yushi2.web.engr.illinois.edu/.