Topic-aware latent models for representation learning on networks

TL;DR

This paper introduces TNE, a framework that enhances network node embeddings by integrating topic-based community information, improving performance in node classification and link prediction tasks.

Contribution

The paper proposes a novel topic-aware embedding framework for network representation learning that combines community detection with random walk-based methods.

Findings

Outperforms baseline models in node classification

Achieves higher accuracy in link prediction

Effectively incorporates community information into embeddings

Abstract

Network representation learning (NRL) methods have received significant attention over the last years thanks to their success in several graph analysis problems, including node classification, link prediction, and clustering. Such methods aim to map each vertex of the network into a low-dimensional space in a way that the structural information of the network is preserved. Of particular interest are methods based on random walks; such methods transform the network into a collection of node sequences, aiming to learn node representations by predicting the context of each node within the sequence. In this paper, we introduce TNE, a generic framework to enhance the embeddings of nodes acquired by means of random walk-based approaches with topic-based information. Similar to the concept of topical word embeddings in Natural Language Processing, the proposed model first assigns each node to a latent community with the favor of various statistical graph models and community detection methods and then learns the enhanced topic-aware representations. We evaluate our methodology in two downstream tasks: node classification and link prediction. The experimental results demonstrate that by incorporating node and community embeddings, we are able to outperform widely-known baseline NRL models.