A Distributed GPU-based Correlation Clustering Algorithm for Large-scale Signed Social Networks
When applied to signed networks, the Correlation Clustering (CC) problem consists of an important tool to study how balanced a social group behaves and if this group might evolve to a possible balanced state. Solving such combinatorial optimization problem is a challenging task, which heavily relies on heuristic procedures, one of the few solution methods capable of analyzing large network instances. In this work, we present a novel approach to solve the CC problem on large-scale signed networks. A distributed GPU-powered version of the ILS metaheuristic, which benefits from data parallelism, has been developed. This technique provides good quality clustering results when compared to non-distributed methods. Experiments were conducted on both synthetic and real datasets. The proposed algorithm achieved improved solution values when compared to the existing parallel solution method. In particular, one of the largest graphs we have considered in our experiments contains 1 million nodes and 8 million edges – such graph can be clustered in two hours using our algorithm. The new method can process networks for which there is no efficient solution using the existing algorithms found in the literature.
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