Algorithms for Community Detection in Complex Networks

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Analysis of Algorithms and Complexity Theory".

Deadline for manuscript submissions: closed (31 May 2017) | Viewed by 42974

Special Issue Editor


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Guest Editor
Institute of Theoretical Informatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Interests: scalable graph algorithms; algorithm engineering; algorithmic network analysis and synthesis; combinatorial scientific computing; parallel combinatorial optimization

Special Issue Information

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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Published Papers (5 papers)

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14 pages, 288 KiB  
Article
Scale Reduction Techniques for Computing Maximum Induced Bicliques
Algorithms 2017, 10(4), 113; https://doi.org/10.3390/a10040113 - 4 Oct 2017
23 pages, 605 KiB  
Article
Mapping Higher-Order Network Flows in Memory and Multilayer Networks with Infomap
Algorithms 2017, 10(4), 112; https://doi.org/10.3390/a10040112 - 30 Sep 2017
26 pages, 840 KiB  
Article
Local Community Detection in Dynamic Graphs Using Personalized Centrality
Algorithms 2017, 10(3), 102; https://doi.org/10.3390/a10030102 - 29 Aug 2017
21 pages, 6529 KiB  
Article
Post-Processing Partitions to Identify Domains of Modularity Optimization
Algorithms 2017, 10(3), 93; https://doi.org/10.3390/a10030093 - 19 Aug 2017
22 pages, 464 KiB  
Article
Local Community Detection Based on Small Cliques
Algorithms 2017, 10(3), 90; https://doi.org/10.3390/a10030090 - 11 Aug 2017
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