Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

Graph self-supervised learning (SSL), including contrastive and generative approaches,
offers great potential to address the fundamental challenge of label scarcity in real-world …

Recent deep learning approaches for representation learning on graphs follow a
neighborhood aggregation procedure. We analyze some important properties of these …

Graph embedding methods produce unsupervised node features from graphs that can then
be used for a variety of machine learning tasks. However, modern graph datasets contain …

Numerous important problems can be framed as learning from graph data. We propose a
framework for learning convolutional neural networks for arbitrary graphs. These graphs …

Community detection in networks is one of the most popular topics of modern network
science. Communities, or clusters, are usually groups of vertices having higher probability of …

Local graph clustering methods aim to find a cluster of nodes by exploring a small region of
the graph. These methods are attractive because they enable targeted clustering around a …

Networks are a fundamental tool for understanding and modeling complex systems in
physics, biology, neuroscience, engineering, and social science. Many networks are known …

Real networks exhibit heterogeneous nature with nodes playing far different roles in
structure and function. To identify vital nodes is thus very significant, allowing us to control …