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The k-truss is a type of cohesive subgraphs proposed recently for the studyof networks. While the problem of computing most cohesive subgraphs is NP-hard,there exists a polynomial time algorithm for computing k-truss. Compared withk-core which is also efficient to compute, k-truss represents the "core" of ak-core that keeps the key information of, while filtering out less importantinformation from, the k-core. However, existing algorithms for computingk-truss are inefficient for handling today's massive networks. We first improvethe existing in-memory algorithm for computing k-truss in networks of moderatesize. Then, we propose two I/O-efficient algorithms to handle massive networksthat cannot fit in main memory. Our experiments on real datasets verify theefficiency of our algorithms and the value of k-truss.

Truss Decomposition in Massive Networks