

CONVERDENSE: Framework of Convergence Summary Graph for Large Numbers of Networks
Abstract
graphs. However, these approaches encounter scalability and interpretability issues when being applied to massive networks: (1) The number of frequent dense subgraphs is explosive when there are very large frequent dense subgraphs, e.g. subgraphs with hundreds of edges. (2) A frequent dense subgraph may not represent a tight association among its nodes. We develop a novel algorithm, to mine convergence dense subgraphs, a concept having better interpretability than frequent graph. All edges in a convergence subgraph should exhibit correlated occurrences in the whole graph set.
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