Algorithms for Community Detection in Complex Networks

Dear Colleagues,

Complex networks, such as social networks or web graphs, are characterized by a heterogeneous topology. Often, this leads to a low diameter, a high clustering coefficient, and a heavy-tailed degree distribution. Such networks also often feature (a hierarchy of) communities or clusters, i.e., vertex subsets that have many internal connections and relatively few external ones. Computing meaningful communities is a non-trivial task, often phrased as an optimization problem. High-quality solutions are sought for many applications in various fields; devising suitable algorithms has thus been an active research area for quite some time now.

This Special Issue shall reflect recent algorithmic advancements in the field, in particular for scenarios beyond disjoint communities in static undirected one-layer networks. We invite original high-quality research on all algorithmic aspects (both theoretical and applied) of community detection in complex networks, including (but not limited to):

• overlapping community detection
• dynamic community detection
• local community detection
• multi-objective community detection
• community detection in directed and/or multilayer networks
• exact, approximate, and heuristic methods
• complexity of community detection tasks
• methods combining graph topology and semantic data (attributes)
• community detection methodology, e.g. novel objective functions
• experimental methodology for community detection
• parallel and distributed algorithms for community detection

Authors should consult the journal's submission policy. Submissions based on papers that appeared before in published conference proceedings are allowed, provided that this is indicated clearly and that the submission has been extended significantly (by at least 40% new material).

Prof. Dr. Henning Meyerhenke
Guest Editor

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