Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences

TL;DR

This paper introduces MNCI, an inductive method for generating dynamic node embeddings in temporal networks by combining neighborhood and community influences, outperforming existing methods on multiple tasks.

Contribution

The paper presents a novel inductive network embedding approach that effectively integrates neighborhood and community influences in temporal networks.

Findings

MNCI outperforms baseline methods in node classification.

MNCI achieves better network visualization results.

The method effectively captures temporal dynamics in networks.

Abstract

Network representation learning aims to generate an embedding for each node in a network, which facilitates downstream machine learning tasks such as node classification and link prediction. Current work mainly focuses on transductive network representation learning, i.e. generating fixed node embeddings, which is not suitable for real-world applications. Therefore, we propose a new inductive network representation learning method called MNCI by mining neighborhood and community influences in temporal networks. We propose an aggregator function that integrates neighborhood influence with community influence to generate node embeddings at any time. We conduct extensive experiments on several real-world datasets and compare MNCI with several state-of-the-art baseline methods on various tasks, including node classification and network visualization. The experimental results show that MNCI achieves better performance than baselines.