Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs

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Abstract:

Summarizing large-scaled directed graphs into small-scale representations is a useful but less studied problem setting. Conventional clustering approaches, which based on "Min-Cut"-style criteria, compress both the vertices and edges of the graph into the communities, that lead to a loss of directed edge information. On the other hand, ...More

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