Multiway Autoregressive Network: A Dynamic Graph Representation Framework for Temporal Link Prediction.
TL;DR
A novel theoretical model, namely the multiway autoregressive (MARS) model, which characterizes multiple evolutionary paths by capturing dependencies within and across two core factors underlying diverse evolutionary mechanisms is proposed, which develops a general DGNN framework, a multiway autoregressive network (MAN).
Abstract
Understanding how links form and predicting future link states is of great importance in social, traffic, and many other complex temporal networks. These temporal networks are typically governed by multiple evolutionary mechanisms. However, existing dynamic graph representation methods, especially dynamic graph neural networks (DGNNs), are constrained by their single-evolutionary-path architecture, where spatial and temporal dynamics are either sequentially stacked or integrated into a monolithic module in a fixed manner. This design not only restricts the exploration of diverse evolutionary paths arising from rich evolutionary mechanisms but also confines spatiotemporal interactions to passive and implicit modeling, resulting in compromised performance and weak interpretability. To address these issues, we propose a novel theoretical model, namely the multiway autoregressive (MARS) model, which characterizes multiple evolutionary paths by capturing dependencies within and across two core factors underlying diverse evolutionary mechanisms. Based on this theoretical foundation, we develop a general DGNN framework, a multiway autoregressive network (MAN), by transforming the network architecture into a 2-D diagram that characterizes evolutionary dependencies in the spatiotemporal domain. Each node encodes an evolving state of the dynamic graph representation, while each edge denotes an evolutionary transition from one state to another. Moreover, three elementary evolutionary operators are incorporated into edges along distinct directions, capturing spatialwise, temporalwise, and cross-spatiotemporal evolutionary dynamics, respectively. This enables researchers to develop a variety of DGNNs by configuring the evolutionary operators in different ways. To validate the effectiveness of this framework, we design a new DGNN, which employs a graph convolutional network (GCN), a gated recurrent unit (GRU), and our proposed time-delayed GCN (TD-GCN) as core components. Promising experimental results demonstrate that the proposed approach achieves state-of-the-art temporal link prediction performance on both synthetic and real-world temporal networks across diverse domains.