Drawing clustered graphs by preserving neighborhoods

Eli Parviainen, Jari Saramäki

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)

Abstract

Weighted graphs with presumed cluster structure are challenging to many existing graph drawing methods, even though ways of visualizing such graphs would be much needed in complex networks research. In the field of dimension reduction, t-distibuted stochastic neighbor embedding (t-SNE) has proven successful in visualizing clustered data. Here, we extend t-SNE into graph-SNE (GSNE). Our method builds on the sensitivity of random walks to cluster structure in graphs. We use random walks to define a neighborhood probability that realizes the properties behind the success of t-SNE in visualizing clustered data sets: Gaussian-like behavior of neighborhood probabilities, adaptation to local edge density, and an adjustable granularity scale. We show that GSNE correctly visualizes artificial graphs where ground-truth cluster structure is known. Using real-world networks, we show that GSNE is able to produce meaningful visualizations that display plausible cluster structure which is not captured by state-of-the-art visualization methods.
Original languageEnglish
Pages (from-to)174 - 180
JournalPattern Recognition Letters
Volume100
Issue numberSupplement C
DOIs
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Graph drawing
  • Stochastic neighbor embedding
  • Clustered network
  • Random walk
  • Similarity

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