An adaptive granulation algorithm for community detection based on improved label propagation

摘要

Community detection is a hot research in complex network analysis. Detecting community structure in networks is crucial for insight into the internal connections within networks. A variety of algorithms have previously been proposed, while few of them can efficiently apply to large-scale networks due to unacceptable running time and intractable parameter tuning. To tackle the above issues, this paper proposes an adaptive granulation algorithm for community detection based on improved label propagation (Gr-ILP), which granulates a network hierarchically with the improved label propagation strategy. For a given network, first, an improved label propagation strategy (ILP) is adopted to gather similar nodes into non-overlapping collections which consist of nodes with high similarity. Second, each collection detected in first step is granulated into a super node, and the edges between two collections are granulated into a super edge. After this granulation processing, a super-network that is coarser and smaller than the original one is formed. Then, the above two steps are repeated iteratively until it stops forming new collections in the first step. Due to the adoption of an adaptive strategy, the proposed Gr-ILP algorithm granulates the network to a certain layer which saves much time when processing large-scale networks. Finally, Gr-ILP assigns unallocated and isolated nodes to the appropriate community. The proposed algorithm requires neither any priori information of communities nor adjustment of any parameters and still can obtained satisfactory community structure adaptively. Gr-ILP tends to preserve small-scale communities by limiting the growth of node collections. Moreover, because of the sharp decline in network size by the granulation process, the algorithm consumes less time and is suitable for large-scale networks. Experimental results on eight real-world network datasets of different types and sizes demonstrate the effectiveness and efficiency of our algorithm, compared with several other baseline algorithms. (C) 2019 Elsevier Inc. All rights reserved.