Learning Vertex Representations for Bipartite Networks

TL;DR

This paper addresses the gap in network representation learning for bipartite networks by proposing specialized methods to better capture the unique relationships between two distinct entity types.

Contribution

The paper introduces novel vertex embedding techniques tailored for bipartite networks, improving upon generic methods that ignore vertex type differences.

Findings

Enhanced embedding quality for bipartite networks

Improved performance in downstream tasks like recommendation

Demonstrated superiority over generic embedding methods

Abstract

Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages.