Temporal networks have been widely used to model real-world complex systems such as
financial systems and e-commerce systems. In a temporal network, the joint neighborhood of …

This study introduces a novel computational framework integrating monadic second-order
temporal logic (MSOTL) with hypergraph models to enhance the predictive analysis and …

Subgraph-based graph representation learning (SGRL) has been recently proposed to deal
with some fundamental challenges encountered by canonical graph neural networks …

Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful
tool in many prediction tasks on graphs due to its advantages in model expressiveness and …

Temporal hypergraphs provide a powerful paradigm for modeling time-dependent, higher-
order interactions in complex systems. Representation learning for hypergraphs is essential …

It is demonstrated that higher-order patterns beyond pairwise relations can significantly
enhance the learning capability of existing graph-based models, and simplex is one of the …

To handle graphs in which features or connections are evolving over time, a series of
temporal graph neural networks (TGNNs) have been proposed. Despite the success of …

Hyperedges, as extensions of pairwise edges, can characterize higher-order relations
among multiple individuals. Due to the necessity of hypergraph detection in practical …

Temporal graph representation learning aims to generate low-dimensional dynamic node
embeddings to capture temporal information as well as structural and property information …

In this paper, we provide a theory of using graph neural networks (GNNs) for\textit {multi-
node representation learning}, where we are interested in learning a representation for a set …