TriNE: Network Representation Learning for Tripartite Heterogeneous Networks

TL;DR

This paper introduces TriNE, a novel network embedding method for tripartite heterogeneous networks that captures both explicit and implicit relationships among three types of nodes, improving prediction tasks.

Contribution

The paper proposes TriNE, a tripartite network embedding approach that models complex relationships using metapath-guided random walks and a heterogeneous skip-gram model.

Findings

TriNE outperforms existing methods in user response prediction.

Effective modeling of explicit and implicit tripartite relationships.

Validated on real-world tripartite networks.

Abstract

In this paper, we study network representation learning for tripartite heterogeneous networks which learns node representation features for networks with three types of node entities. We argue that tripartite networks are common in real world applications, and the essential challenge of the representation learning is the heterogeneous relations between various node types and links in the network. To tackle the challenge, we develop a tripartite heterogeneous network embedding called TriNE. The method considers unique user-item-tag tripartite relationships, to build an objective function to model explicit relationships between nodes (observed links), and also capture implicit relationships between tripartite nodes (unobserved links across tripartite node sets). The method organizes metapath guided random walks to create heterogeneous neighborhood for all node types in the network. This information is then utilized to train a heterogeneous skip-gram model based on a joint optimization. Experiments on real-world tripartite networks validate the performance of TriNE for the online user response prediction using embedding node features.