DepthRank: Exploiting Temporality to Uncover Important Network Nodes

  • Nikolaos BastasEmail author
  • Theodoros Semertzidis
  • Petros Daras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)


Identifying important network nodes is very crucial for a variety of applications, such as the spread of an idea or an innovation. The majority of the publications so far assume that the interactions between nodes are static. However, this approach neglects that real-world phenomena evolve in time. Thus, there is a need for tools and techniques which account for evolution over time. Towards this direction, we present a novel graph-based method, named DepthRank (DR) that incorporates the temporal characteristics of the underlying datasets. We compare our approach against two baseline methods and find that it efficiently recovers important nodes on three real world datasets, as indicated by the numerical simulations. Moreover, we perform our analysis on a modified version of the DBLP dataset and verify its correctness using ground truth data.


Influence detection Network analysis Temporal awareness 



This research was performed under the EU’s project “Trusted, Citizen - LEA collaboration over sOcial Networks(TRILLION)” (grant agreement No 653256).

Supplementary material


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nikolaos Bastas
    • 1
    Email author
  • Theodoros Semertzidis
    • 1
  • Petros Daras
    • 1
  1. 1.Centre for Research and Technology HellasThessalonikiGreece

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