Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs

TL;DR

Persona2vec is a novel graph embedding framework that learns multiple node representations to better capture overlapping community structures and node roles, outperforming existing models in speed and accuracy.

Contribution

It introduces a flexible multi-role embedding method that efficiently models complex node characteristics in graphs with overlapping communities.

Findings

Faster than existing state-of-the-art models.

Achieves better link prediction performance.

Effectively captures multi-role node characteristics.

Abstract

Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that a single representation is enough to capture all characteristics of the node. However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. Using link prediction-based evaluation, we show that our framework is significantly faster than the existing state-of-the-art model while achieving better performance.