Special Issue "Algorithms for Community Detection in Complex Networks"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (31 May 2017)

Special Issue Editor

Guest Editor
Prof. Dr. Henning Meyerhenke

Institute of Theoretical Informatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Website | E-Mail
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 550 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

Open AccessArticle Scale Reduction Techniques for Computing Maximum Induced Bicliques
Algorithms 2017, 10(4), 113; doi:10.3390/a10040113
Received: 16 June 2017 / Revised: 19 September 2017 / Accepted: 27 September 2017 / Published: 4 October 2017
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Abstract
Given a simple, undirected graph G, a biclique is a subset of vertices inducing a complete bipartite subgraph in G. In this paper, we consider two associated optimization problems, the maximum biclique problem, which asks for a biclique of the maximum cardinality in
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Given a simple, undirected graph G, a biclique is a subset of vertices inducing a complete bipartite subgraph in G. In this paper, we consider two associated optimization problems, the maximum biclique problem, which asks for a biclique of the maximum cardinality in the graph, and the maximum edge biclique problem, aiming to find a biclique with the maximum number of edges in the graph. These NP-hard problems find applications in biclustering-type tasks arising in complex network analysis. Real-life instances of these problems often involve massive, but sparse networks. We develop exact approaches for detecting optimal bicliques in large-scale graphs that combine effective scale reduction techniques with integer programming methodology. Results of computational experiments with numerous real-life network instances demonstrate the performance of the proposed approach. Full article
(This article belongs to the Special Issue Algorithms for Community Detection in Complex Networks)
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Open AccessFeature PaperArticle Mapping Higher-Order Network Flows in Memory and Multilayer Networks with Infomap
Algorithms 2017, 10(4), 112; doi:10.3390/a10040112
Received: 15 June 2017 / Revised: 15 September 2017 / Accepted: 26 September 2017 / Published: 30 September 2017
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Abstract
Comprehending complex systems by simplifying and highlighting important dynamical patterns requires modeling and mapping higher-order network flows. However, complex systems come in many forms and demand a range of representations, including memory and multilayer networks, which in turn call for versatile community-detection algorithms
[...] Read more.
Comprehending complex systems by simplifying and highlighting important dynamical patterns requires modeling and mapping higher-order network flows. However, complex systems come in many forms and demand a range of representations, including memory and multilayer networks, which in turn call for versatile community-detection algorithms to reveal important modular regularities in the flows. Here we show that various forms of higher-order network flows can be represented in a unified way with networks that distinguish physical nodes for representing a complex system’s objects from state nodes for describing flows between the objects. Moreover, these so-called sparse memory networks allow the information-theoretic community detection method known as the map equation to identify overlapping and nested flow modules in data from a range of different higher-order interactions such as multistep, multi-source, and temporal data. We derive the map equation applied to sparse memory networks and describe its search algorithm Infomap, which can exploit the flexibility of sparse memory networks. Together they provide a general solution to reveal overlapping modular patterns in higher-order flows through complex systems. Full article
(This article belongs to the Special Issue Algorithms for Community Detection in Complex Networks)
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Open AccessArticle Local Community Detection in Dynamic Graphs Using Personalized Centrality
Algorithms 2017, 10(3), 102; doi:10.3390/a10030102
Received: 31 May 2017 / Revised: 22 August 2017 / Accepted: 23 August 2017 / Published: 29 August 2017
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Abstract
Analyzing massive graphs poses challenges due to the vast amount of data available. Extracting smaller relevant subgraphs allows for further visualization and analysis that would otherwise be too computationally intensive. Furthermore, many real data sets are constantly changing, and require algorithms to update
[...] Read more.
Analyzing massive graphs poses challenges due to the vast amount of data available. Extracting smaller relevant subgraphs allows for further visualization and analysis that would otherwise be too computationally intensive. Furthermore, many real data sets are constantly changing, and require algorithms to update as the graph evolves. This work addresses the topic of local community detection, or seed set expansion, using personalized centrality measures, specifically PageRank and Katz centrality. We present a method to efficiently update local communities in dynamic graphs. By updating the personalized ranking vectors, we can incrementally update the corresponding local community. Applying our methods to real-world graphs, we are able to obtain speedups of up to 60× compared to static recomputation while maintaining an average recall of 0.94 of the highly ranked vertices returned. Next, we investigate how approximations of a centrality vector affect the resulting local community. Specifically, our method guarantees that the vertices returned in the community are the highly ranked vertices from a personalized centrality metric. Full article
(This article belongs to the Special Issue Algorithms for Community Detection in Complex Networks)
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Open AccessArticle Post-Processing Partitions to Identify Domains of Modularity Optimization
Algorithms 2017, 10(3), 93; doi:10.3390/a10030093
Received: 5 June 2017 / Revised: 24 July 2017 / Accepted: 15 August 2017 / Published: 19 August 2017
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Abstract
We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics. Given a set of partitions, CHAMP identifies the domain of modularity optimization for each partition—i.e.,
[...] Read more.
We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics. Given a set of partitions, CHAMP identifies the domain of modularity optimization for each partition—i.e., the parameter-space domain where it has the largest modularity relative to the input set—discarding partitions with empty domains to obtain the subset of partitions that are “admissible” candidate community structures that remain potentially optimal over indicated parameter domains. Importantly, CHAMP can be used for multi-dimensional parameter spaces, such as those for multilayer networks where one includes a resolution parameter and interlayer coupling. Using the results from CHAMP, a user can more appropriately select robust community structures by observing the sizes of domains of optimization and the pairwise comparisons between partitions in the admissible subset. We demonstrate the utility of CHAMP with several example networks. In these examples, CHAMP focuses attention onto pruned subsets of admissible partitions that are 20-to-1785 times smaller than the sets of unique partitions obtained by community detection heuristics that were input into CHAMP. Full article
(This article belongs to the Special Issue Algorithms for Community Detection in Complex Networks)
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Open AccessArticle Local Community Detection Based on Small Cliques
Algorithms 2017, 10(3), 90; doi:10.3390/a10030090
Received: 21 June 2017 / Revised: 3 August 2017 / Accepted: 4 August 2017 / Published: 11 August 2017
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Abstract
Community detection aims to find dense subgraphs in a network. We consider the problem of finding a community locally around a seed node both in unweighted and weighted networks. This is a faster alternative to algorithms that detect communities that cover the whole
[...] Read more.
Community detection aims to find dense subgraphs in a network. We consider the problem of finding a community locally around a seed node both in unweighted and weighted networks. This is a faster alternative to algorithms that detect communities that cover the whole network when actually only a single community is required. Further, many overlapping community detection algorithms use local community detection algorithms as basic building block. We provide a broad comparison of different existing strategies of expanding a seed node greedily into a community. For this, we conduct an extensive experimental evaluation both on synthetic benchmark graphs as well as real world networks. We show that results both on synthetic as well as real-world networks can be significantly improved by starting from the largest clique in the neighborhood of the seed node. Further, our experiments indicate that algorithms using scores based on triangles outperform other algorithms in most cases. We provide theoretical descriptions as well as open source implementations of all algorithms used. Full article
(This article belongs to the Special Issue Algorithms for Community Detection in Complex Networks)
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