Speeding up graph clustering via modular decomposition based compression
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Large graphs are difficult to represent, visualize, and understand. In this paper, we introduce â聙聹gate graphâ聙聺 - a new approach to perform graph simplification. A gate graph provides a simplified topological view of the original graph. Specifically, we construct a gate graph from a large graph so that for any â聙聹non-localâ聙聺 vertex pair (distance greater than some threshold) in the original graph, their shortest-path distance can be recovered by consecutive â聙聹localâ聙聺 walks through the gate vertices in the gate graph. We perform a theoretical investigation on the gate-vertex set discovery problem. We characterize its computational complexity and reveal the upper bound of minimum gate vertex set using VC-dimension theory. We propose an efficient mining algorithm to discover a gate-vertex set with guaranteed logarithmic bound. The detailed experimental results using both real and synthetic graphs demonstrate the effectiveness and efficiency of our approach.