Randomized shortest paths with net flows and capacity constraints

Sylvain Courtain*, Pierre Leleux, Ilkka Kivimäki, Guillaume Guex, Marco Saerens

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

This work extends the randomized shortest paths (RSP) model by investigating the net flow RSP and adding capacity constraints on edge flows. The standard RSP is a model of movement, or spread, through a network interpolating between a random-walk and a shortest-path behavior (Kivimäki et al., 2014; Saerens et al., 2009; Yen et al., 2008). The framework assumes a unit flow injected into a source node and collected from a target node with flows minimizing the expected transportation cost, together with a relative entropy regularization term. In this context, the present work first develops the net flow RSP model considering that edge flows in opposite directions neutralize each other (as in electric networks), and proposes an algorithm for computing the expected routing costs between all pairs of nodes. This quantity is called the net flow RSP dissimilarity measure between nodes. Experimental comparisons on node clustering tasks indicate that the net flow RSP dissimilarity is competitive with other state-of-the-art dissimilarities. In the second part of the paper, it is shown how to introduce capacity constraints on edge flows, and a procedure is developed to solve this constrained problem by exploiting Lagrangian duality. These two extensions should improve significantly the scope of applications of the RSP framework.

Original languageEnglish
Pages (from-to)341-360
Number of pages20
JournalInformation Sciences
Volume556
Early online date2021
DOIs
Publication statusPublished - May 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • Complex networks
  • Distances between nodes
  • Graph mining
  • Link analysis
  • Network data analysis
  • Network science

Fingerprint Dive into the research topics of 'Randomized shortest paths with net flows and capacity constraints'. Together they form a unique fingerprint.

Cite this