Title: Learning Vertex Representations for Bipartite Networks,
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. This work addresses the
research gap of learning vertex representations for bipartite networks. We
present a new solution BiNE, short for Bipartite Network Embedding}, which
accounts for two special properties of bipartite networks: long-tail
distribution of vertex degrees and implicit connectivity relations between
vertices of the same type. Technically speaking, we make three contributions:
(1) We design a biased random walk generator to generate vertex sequences that
preserve the long-tail distribution of vertices; (2) We propose a new
optimization framework by simultaneously modeling the explicit relations (i.e.,
observed links) and implicit relations (i.e., unobserved but transitive links);
(3) We explore the theoretical foundations of BiNE to shed light on how it
works, proving that BiNE can be interpreted as factorizing multiple matrices.