CapMax: A Framework for Dynamic Network Representation Learning From the View of Multiuser Communication.

Abstract

In this article, a modified mutual information maximization (InfoMax) framework, named channel capacity maximization (CapMax), is proposed and applied to learn informative representations for dynamic networks with time-varying topology and/or time-evolving node attributes. The CapMax is based on the network information theory for multiuser communication, where the representation model is treated as a multiaccess communication channel with memory and feedback. Without requirements of the backbone structure, the learning objective of our CapMax is maximizing the channel capacity, which is measured by directed information (DI) rather than mutual information. For efficient implementation, we design an estimator of the channel capacity through the combination of graph neural networks (GNNs) and recurrent neural networks (RNNs). Under some mild conditions, we theoretically prove that DI is a better measure than mutual information in capturing useful information. The experiments are conducted on multiple real-world dynamic network datasets, and the outperformance of our CapMax on different backbone models on link detection and prediction validates the effectiveness of modeling the representation model as a communication channel.