Matching through Embedding in Dense Graphs

Abstract Finding optimal matchings in dense graphs is of general interest and of particular importance in social, transportation and biological networks. While developing optimal solutions for various matching problems is important, the running times of the fastest available optimal matching algorithms are too costly. However, when the vertices of the graphs are point-sets in Rd and edge weights correspond to the euclidean distances, the available optimal matching algorithms are substantially faster. In this paper, we propose a novel network embedding based heuristic algorithm to solve various matching problems in dense graphs. In particular, using existing network embedding techniques, we first find a low dimensional representation of the graph vertices in Rd and then run faster available matching algorithms on the embedded vertices. To the best of our knowledge, this is the first work that applies network embedding to solve various matching problems. Experimental results validate the efficacy of our proposed algorithm.
  • Nitish Panigrahy (UMass)
  • Prithwish Basu (BBN)
  • Don Towsley (UMass)
Date Nov-2020
Venue LMCA at NeurIps 2020 [link]