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We investigate the community detection problem on graphs in the existence ofmultiple edge types. Our main motivation is that similarity between objects canbe defined by many different metrics and aggregation of these metrics into asingle one poses several important challenges, such as recovering thisaggregation function from ground-truth, investigating the space of differentclusterings, etc. In this paper, we address how to find an aggregation functionto generate a composite metric that best resonates with the ground-truth. Wedescribe two approaches: solving an inverse problem where we try to findparameters that generate a graph whose clustering gives the ground-truthclustering, and choosing parameters to maximize the quality of the ground-truthclustering. We present experimental results on real and synthetic benchmarks.

Computing an Aggregate Edge-Weight Function for Clustering Graphs with Multiple Edge Types