Community Detection Using Synthetic Coordinates and Flow Propagation

  • Paraskevi FragopoulouEmail author
  • Harris Papadakis
  • Costas Panagiotakis
Part of the Emergence, Complexity and Computation book series (ECC, volume 24)


Various applications like finding web communities, detecting the structure of social networks , or even analyzing a graph’s structure to uncover Internet attacks are just some of the applications for which community detection is important. In this paper, we propose an algorithm that finds the entire community structure of a network, based on local interactions between neighboring nodes and on an unsupervised distributed hierarchical clustering algorithm. In this paper, we describe two novel community detection algorithms, one for full graph communities detection and one for single community detection. The novelty of the first proposed approach, named SCCD (to stand for Synthetic Coordinate Community Detection), is the fact that the algorithm is based on the use of Vivaldi synthetic network coordinates computed by a distributed algorithm . We also present an extended version of said algorithm, modified to deal efficiently with community detection on dynamic graphs . Finally, we present a new algorithm which partially analyzes a graph to detect the community of a single node. The current paper not only presents two efficient community finding algorithms, but also demonstrates that synthetic network coordinates could be used to derive efficient solutions to a variety of problems. Experimental results and comparisons with other methods from the literature are presented for a variety of benchmark graphs with known community structure, derived by varying a number of graph parameters and real dataset graphs. The experimental results and comparisons to existing methods with similar computation cost on real and synthetic data sets demonstrate the high performance and robustness of the proposed scheme.


Node Degree Community Detection Initial Node Hierarchical Cluster Algorithm Community Detection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research has been partially co-financed by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Programs: ARCHIMEDE III-TEI-Crete-P2PCOORD.


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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Paraskevi Fragopoulou
    • 1
    • 2
    Email author
  • Harris Papadakis
    • 1
  • Costas Panagiotakis
    • 2
    • 3
  1. 1.Department of Informatics EngineeringTechnological Educational Institute of CreteHeraklionGreece
  2. 2.Foundation for Research and Technology-HellasInstitute of Computer ScienceHeraklionGreece
  3. 3.Department of Business AdministrationTechnological Educational Institute of CreteAgios NikolaosGreece

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