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With invaluable theoretical and practical benefits, the problem of partitioning networks for community structures has attracted significant research attention in scientific and engineering disciplines. In literature, Newmans modularity measure is routinely applied to quantify the quality of a given partition, and thereby maximizing the measure provides a principled way of detecting communities in networks. Unfortunately, the exact optimization of the measure is computationally NP-complete and only applicable to very small networks. Approximation approaches have to be sought to scale to large networks. To address the computational issue, we proposed a new method to identify the partition decisions. Coupled with an iterative rounding strategy and a fast constrained power method, our work achieves tight and effective spectral relaxations. The proposed method was evaluated thoroughly on both real and synthetic networks. Compared with state-of-the-art approaches, the method obtained comparable, if not better, qualities. Meanwhile, it is highly suitable for parallel execution and reported a nearly linear improvement in running speed when increasing the number of computing nodes, which thereby provides a practical tool for partitioning very large networks.
A distinguishing property of communities in networks is that cycles are more prevalent within communities than across communities. Thus, the detection of these communities may be aided through the incorporation of measures of the local richness of th
We present a network community-detection technique based on properties that emerge from a nature-inspired system of aligning particles. Initially, each vertex is assigned a random-direction unit vector. A nonlinear dynamic law is established so that
We study the structure of loops in networks using the notion of modulus of loop families. We introduce a new measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among
We apply spectral clustering and multislice modularity optimization to a Los Angeles Police Department field interview card data set. To detect communities (i.e., cohesive groups of vertices), we use both geographic and social information about stops
Community structures are critical towards understanding not only the network topology but also how the network functions. However, how to evaluate the quality of detected community structures is still challenging and remains unsolved. The most widely