Learning resolution parameters for graph clustering
N Veldt, DF Gleich, A Wirth, Ling Liu (ed.), Ryen White (ed.)
WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019) | ACM | Published : 2019
Finding clusters of well-connected nodes in a graph is an extensively studied problem in graph-based data analysis. Because of its many applications, a large number of distinct graph clustering objective functions and algorithms have already been proposed and analyzed. To aid practitioners in determining the best clustering approach to use in different applications, we present new techniques for automatically learning how to set clustering resolution parameters. These parameters control the size and structure of communities that are formed by optimizing a generalized objective function. We begin by formalizing the notion of a parameter fitness function, which measures how well a fixed input ..View full abstract
Awarded by NSF
The authors thank several funding agencies: Nate Veldt is supported by NSF award IIS-154648, David Gleich is supported by the DARPA SIMPLEX program, the Sloan Foundation, and NSF awards CCF- 1149756, CCF-0939370, and IIS-154648. Anthony Wirth is funded by the Melbourne School of Engineering.