Finding the proper depth $ d $ of a graph convolutional network (GCN) that provides strong
representation ability has drawn significant attention, yet nonetheless largely remains an …

Network embedding plays a significant role in a variety of applications. To capture the
topology of the network, most of the existing network embedding algorithms follow a …

Graph neural networks (GNNs) have exhibited superb power in many graph related tasks.
Existing GNNs can be categorized into spatial GNNs and spectral GNNs. The spatial GNNs …

Network embedding plays an important role in a variety of social network applications.
Existing network embedding methods, explicitly or implicitly, can be categorized into …

This paper investigates the few-shot temporal reasoning capability within the hyperbolic
space. The goal is to forecast future events for newly emerging entities within temporal …

This paper addresses influence pathway discovery, a key emerging problem in today's
online media. We propose a discovery algorithm that leverages recently published work on …

Temporal graphs, depicting time-evolving node connections through temporal edges, are
extensively utilized in domains where temporal connection patterns are essential, such as …

Network embedding is a commonly used technique in graph mining and plays an important
role in a variety of applications. Most network embedding works can be categorized into …

Graph-structured data are prevalent in a wide range of application domains, as many data
inherently demonstrate interconnected patterns. With recent advances in artificial …