Abstract
This paper considers the problem of finding large dense subgraphs in relational graphs, i.e., a set of graphs which share a common vertex set. We present an approximation algorithm for finding the densest common subgraph in a relational graph set based on an extension of Charikar’s method for finding the densest subgraph in a single graph. We also present a simple greedy heuristic which can be implemented efficiently for analysis of larger graphs. We give graph dependent bounds on the quality of the solutions returned by our methods. Lastly, we show by empirical evaluation on several benchmark datasets that our method out-performs existing approaches.
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Jethava, V., Beerenwinkel, N. (2015). Finding Dense Subgraphs in Relational Graphs. In: Appice, A., Rodrigues, P., Santos Costa, V., Gama, J., Jorge, A., Soares, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9285. Springer, Cham. https://doi.org/10.1007/978-3-319-23525-7_39
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