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 …

Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …

Attributed graph embedding, which learns vector representations from graph topology and
node features, is a challenging task for graph analysis. Recently, methods based on graph …

Network embedding, aiming to learn the low-dimensional representations of nodes in
networks, is of paramount importance in many real applications. One basic requirement of …

Attributed graph clustering is a prominent research area, catering to the increasing need for
understanding real-world systems by uncovering exhaustive meaningful latent knowledge …

Attributed graph clustering is challenging as it requires joint modelling of graph structures
and node attributes. Recent progress on graph convolutional networks has proved that …

Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

Community detection has become an important research topic in machine learning due to
the proliferation of network data. However, most existing methods have been developed …

Learning on data represented with multiple views (eg, multiple types of descriptors or
modalities) is a rapidly growing direction in machine learning and computer vision. Although …

Community detection is a fundamental and widely-studied problem that finds all densely-
connected groups of nodes and well separates them from others in graphs. With the …