Deep models trained in supervised mode have achieved remarkable success on a variety of
tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …

Graphs are ubiquitous in encoding relational information of real-world objects in many
domains. Graph generation, whose purpose is to generate new graphs from a distribution …

Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a
fundamental yet challenging task. Benefiting from the powerful representation capability of …

Diffusion-based generative graph models have been proven effective in generating high-
quality small graphs. However, they need to be more scalable for generating large graphs …

Graph clustering based on embedding aims to divide nodes with higher similarity into
several mutually disjoint groups, but it is not a trivial task to maximumly embed the graph …

Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as
powerful methods for link prediction. Their performances are less impressive on community …

Deep generative models have achieved great success in areas such as image, speech, and
natural language processing in the past few years. Thanks to the advances in graph-based …

Graph clustering is a longstanding research topic, and has achieved remarkable success
with the deep learning methods in recent years. Nevertheless, we observe that several …

Graph self-supervised learning (SSL) has been vastly employed to learn representations
from unlabeled graphs. Existing methods can be roughly divided into predictive learning and …

In this paper, we consider an inverse problem in graph learning domain--" given the graph
representations smoothed by Graph Convolutional Network (GCN), how can we reconstruct …