Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …

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

Graph representation learning has emerged as a powerful technique for addressing real-
world problems. Various downstream graph learning tasks have benefited from its recent …

Since real-world objects and their interactions are often multi-modal and multi-typed,
heterogeneous networks have been widely used as a more powerful, realistic, and generic …

The increasing popularity of social media has attracted a huge number of people to
participate in numerous activities on a daily basis. This results in tremendous amounts of …

Motivation The anatomical therapeutic chemical (ATC) classification system plays an
increasingly important role in drug repositioning and discovery. The correct identification of …

Abstract Graph Neural Networks (GNNs) are a class of deep models that operates on data
with arbitrary topology and order-invariant structure represented as graphs. We introduce an …

Many applications require to learn, mine, analyze and visualize large-scale graphs. These
graphs are often too large to be addressed efficiently using conventional graph processing …

Local community detection aims to find a set of densely-connected nodes containing given
query nodes. Most existing local community detection methods are designed for a single …

Given a graph G where each node is associated with a set of attributes, attributed network
embedding (ANE) maps each node v∈ G to a compact vector Xv, which can be used in …