Parallel Compression of Weighted Graphs

Large graphs such as social network, web graph, biological network, are complex and facing the challenges of processing and visualization. Motivated by such issues, Taivonen et al. [1] proposed models and sequential algorithms for weighted graph with the intentions to generate a candidate compress graph. The proposed compression algorithm is expensive in terms of computation time because of the sequential process. The weighted graph compression algorithms can be made faster while adopting parallel processing technique. In this paper, we adopt parallel processing technique for weighted graph compression problem while using multi-selection nodes to perform merge-able technique with various graph clustering algorithms to avoid overlapping between nodes from different threads. For the performance evaluation purposes of the proposed method, we carry out series of tests on the real networks. We perform extensive experiments on parallel graph summarization while using different graph clustering algorithms. Our results demonstrate their effectiveness for parallel graph compression on real networks.