Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

TL;DR

Neural-Brane introduces a neural Bayesian ranking approach that combines network topology and node attributes to produce enriched vertex embeddings, improving performance in node classification and clustering tasks.

Contribution

It presents a novel neural network model that unifies attributed and relational information for network embedding using Bayesian personalized ranking.

Findings

Outperforms state-of-the-art methods in node classification.

Achieves superior clustering results on real-world datasets.

Effectively integrates node attributes with topological data.

Abstract

Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as, user profiles of an online social network, or textual contents of a citation network), which is abundant in all real-life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural-Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes; Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node-pair and a dissimilar node-pair. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification and clustering tasks on four real-world datasets. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art existing methods.