Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences
This addresses the need for inductive representation learning in real-world temporal networks, offering an incremental improvement over existing transductive methods.
The paper tackles the problem of generating dynamic node embeddings for temporal networks by proposing MNCI, a method that integrates neighborhood and community influences, and shows it outperforms state-of-the-art baselines in tasks like node classification and network visualization.
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.