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A fast and robust kernel optimization method for core--periphery detection in directed and weighted graphs

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Original languageEnglish
Number of pages13
JournalApplied Network Science
Early online date23 Sep 2019
Publication statusE-pub ahead of print - 23 Sep 2019


Many graph mining tasks can be viewed as classification problems on high
dimensional data. Within this class we consider the issue of discovering
core-periphery structure, which has wide applications in the economic and social
sciences. In contrast to many current approaches, we allow for weighted and
directed edges and we do not assume that the overall network is connected. Our
approach extends recent work on a relevant relaxed nonlinear optimization
problem. In the directed, weighted setting, we derive and analyze a globally
convergent iterative algorithm. We also relate the algorithm to a maximum
likelihood reordering problem on an appropriate core-periphery random graph
model. We illustrate the effectiveness of the new algorithm on a large scale
directed email network.

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