Edge Role Discovery via Higher-Order Structures

  • Nesreen K. Ahmed
  • Ryan A. Rossi
  • Theodore L. Willke
  • Rong Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10234)

Abstract

Previous work in network analysis has focused on modeling the roles of nodes in graphs. In this paper, we introduce edge role discovery and propose a framework for learning and extracting edge roles from large graphs. We also propose a general class of higher-order role models that leverage network motifs. This leads us to develop a novel edge feature learning approach for role discovery that begins with higher-order network motifs and automatically learns deeper edge features. All techniques are parallelized and shown to scale well. They are also efficient with a time complexity of \(\mathcal {O}(|E|)\). The experiments demonstrate the effectiveness of our model for a variety of ML tasks such as improving classification and dynamic network analysis.

Keywords

Role discovery Edge roles Higher-order network analysis Graphlets Network motifs Latent space models Transfer learning 

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nesreen K. Ahmed
    • 1
  • Ryan A. Rossi
    • 2
  • Theodore L. Willke
    • 1
  • Rong Zhou
    • 2
  1. 1.Intel LabsSanta ClaraUSA
  2. 2.Palo Alto Research Center (Xerox PARC)Palo AltoUSA

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