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 …

Given a network, local community detection (aka graph clustering) methods aim at finding
communities around the selected initial nodes (also referred to as seeds, starting nodes or …

The heat kernel is a type of graph diffusion that, like the much-used personalized PageRank
diffusion, is useful in identifying a community nearby a starting seed node. We present the …

We develop new methods based on graph motifs for graph clustering, allowing more
efficient detection of communities within networks. We focus on triangles within graphs, but …

Community detection is an important task in network analysis. A community (also referred to
as a cluster) is a set of cohesive vertices that have more connections inside the set than …

The communities of a social network are sets of vertices with more connections inside the
set than outside. We theoretically demonstrate that two commonly observed properties of …

Network community detection is a hot research topic in network analysis. Although many
methods have been proposed for community detection, most of them only take into …

Community detection (or graph clustering) is crucial for unraveling the structural properties
of complex networks. As an important technique in community detection, label propagation …

In this paper we discuss a very simple approach of combining content and link information in
graph structures for the purpose of community discovery, a fundamental task in network …

The phenomenon of edge clustering in real-world networks is a fundamental property
underlying many ideas and techniques in network science. Clustering is typically quantified …