This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …

Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by
GNNs remains a challenging open problem. The leading method mainly addresses the local …

Temporal graph networks (TGNs) have gained prominence as models for embedding
dynamic interactions, but little is known about their theoretical underpinnings. We establish …

This work formalizes the associational task of predicting node attribute evolution in temporal
graphs from the perspective of learning equivariant representations. We show that node …

Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …

Real-world graphs such as social networks, communication networks, and rating networks
are constantly evolving over time. Many deep learning architectures have been developed …

Recently, multi-view learning has captured widespread attention in the machine learning
area, yet it is still crucial and challenging to exploit beneficial patterns from multi-view data …

The problem of learning and forecasting underlying trends in time series data arises in a
variety of applications, such as traffic management, energy optimization, etc. In literature, a …

Graph Convolutional Networks (GCNs) are a popular method from graph representation
learning that have proved effective for tasks like node classification. Recent variants on …