Dynamic Community Detection via Adversarial Temporal Graph Representation Learning

TL;DR

This paper introduces ATGRL, a novel adversarial temporal graph representation learning framework that effectively detects dynamic communities in brain networks by capturing spatio-temporal features and maximizing modularity.

Contribution

It proposes a new temporal graph attention network with adversarial training for dynamic community detection in brain networks, improving feature learning and modularity optimization.

Findings

Effective detection of dynamic communities in brain networks.

Improved modularity scores compared to baseline methods.

Demonstrated robustness on real-world datasets.

Abstract

Dynamic community detection has been prospered as a powerful tool for quantifying changes in dynamic brain network connectivity patterns by identifying strongly connected sets of nodes. However, as the network science problems and network data to be processed become gradually more sophisticated, it awaits a better method to efficiently learn low dimensional representation from dynamic network data and reveal its latent function that changes over time in the brain network. In this work, an adversarial temporal graph representation learning (ATGRL) framework is proposed to detect dynamic communities from a small sample of brain network data. It adopts a novel temporal graph attention network as an encoder to capture more efficient spatio-temporal features by attention mechanism in both spatial and temporal dimensions. In addition, the framework employs adversarial training to guide the learning of temporal graph representation and optimize the measurable modularity loss to maximize the modularity of community. Experiments on the real-world brain networks datasets are demonstrated to show the effectiveness of this new method.