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Community detection in directed acyclic graphs

  • Leo Speidel
  • Taro Takaguchi
  • Naoki Masuda
Open Access
Regular Article
First Online:
Received:
Revised:
Part of the following topical collections:
  1. Topical issue: Temporal Network Theory and Applications

Abstract

Some temporal networks, most notably citation networks, are naturally represented as directed acyclic graphs (DAGs). To detect communities in DAGs, we propose a modularity for DAGs by defining an appropriate null model (i.e., randomized network) respecting the order of nodes. We implement a spectral method to approximately maximize the proposed modularity measure and test the method on citation networks and other DAGs. We find that the attained values of the modularity for DAGs are similar for partitions that we obtain by maximizing the proposed modularity (designed for DAGs), the modularity for undirected networks and that for general directed networks. In other words, if we neglect the order imposed on nodes (and the direction of links) in a given DAG and maximize the conventional modularity measure, the obtained partition is close to the optimal one in the sense of the modularity for DAGs.

Keywords

Null Model Spectral Method Citation Network Directed Network Jaccard Index 
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.
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Copyright information

© The Author(s) 2015

Authors and Affiliations

  1. 1.Department of Mathematical InformaticsThe University of TokyoTokyoJapan
  2. 2.JST, ERATO, Kawarabayashi Large Graph ProjectTokyoJapan
  3. 3.National Institute of InformaticsTokyoJapan
  4. 4.Department of Engineering MathematicsUniversity of BristolClifton, BristolUK

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