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We study clustering on graphs with multiple edge types. Our main motivationis that similarities between objects can be measured in many different metrics.For instance similarity between two papers can be based on common authors,where they are published, keyword similarity, citations, etc. As such, graphswith multiple edges is a more accurate model to describe similarities betweenobjects. Each edge/metric provides only partial information about the data;recovering full information requires aggregation of all the similarity metrics.Clustering becomes much more challenging in this context, since in addition tothe difficulties of the traditional clustering problem, we have to deal with aspace of clusterings. We generalize the concept of clustering in single-edgegraphs to multi-edged graphs and investigate problems such as: Can we find aclustering that remains good, even if we change the relative weights ofmetrics? How can we describe the space of clusterings efficiently? Can we findunexpected clusterings (a good clustering that is distant from all givenclusterings)? If given the ground-truth clustering, can we recover how theweights for edge types were aggregated? %In this paper, we discuss theseproblems and the underlying algorithmic challenges and propose some solutions.We also present two case studies: one based on papers on Arxiv and one based onCIA World Factbook.

On Clustering on Graphs with Multiple Edge Types