Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Abstract Most Graph Neural Networks (GNNs) predict the labels of unseen graphs by
learning the correlation between the input graphs and labels. However, by presenting a …

Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities
by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …

Graphs have been widely adopted to denote structural connections between entities. The
relations are in many cases heterogeneous, but entangled together and denoted merely as …

Recently, self-supervised learning for graph neural networks (GNNs) has attracted
considerable attention because of their notable successes in learning the representation of …

In the first days of social networking, the typical view of a community was a set of user
profiles of the same interests and likes, and this community kept enlarging by searching …

Graph machine learning has been extensively studied in both academia and industry.
Although booming with a vast number of emerging methods and techniques, most of the …

This paper studies the problem of conducting self-supervised learning for node
representation learning on graphs. Most existing self-supervised learning methods assume …

The existing graph neural architecture search (GNAS) methods heavily rely on supervised
labels during the search process, failing to handle ubiquitous scenarios where supervisions …

Session-based recommendation (SBR) has drawn increasingly research attention in recent
years, due to its great practical value by only exploiting the limited user behavior history in …