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 …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

Deep learning on graphs has recently achieved remarkable success on a variety of tasks,
while such success relies heavily on the massive and carefully labeled data. However …

Inspired by the recent success of self-supervised methods applied on images, self-
supervised learning on graph structured data has seen rapid growth especially centered on …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

In this paper, we propose a simple unsupervised graph representation learning method to
conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss …

Graph Neural Networks (GNNs) have advanced graph structure understanding via recursive
information exchange and aggregation among graph nodes. To improve model robustness …

Recently, with the superior capacity for analyzing the multiplex graph data, self-supervised
multiplex graph representation learning (SMGRL) has received much interest. However …

Recent self-supervised pre-training methods on Heterogeneous Information Networks
(HINs) have shown promising competitiveness over traditional semi-supervised …

Heterogeneous graph convolutional networks have gained great popularity in tackling
various network analytical tasks on heterogeneous network data, ranging from link …