Large-scale community detection based on core node and layer-by-layer label propagation

Abstract

With the vigorous development of big data, the task of large-scale community structure detection is more challenging. In this paper, a large-scale community detection method based on core node and layer-by-layer label propagation is proposed, which is further extended to the detection of overlapping community structures. First, the core nodes whose node degree is greater than the average degree in the graph are found to make effective use of the feature that the core node is the potential community center. This will also avoid the impact of nodes with low node degrees on community structure detection. Then, starting from the core node, label propagation is carried out layer-by-layer according to the node degree and node connection, which effectively improves the accuracy of community detection. The node labels after label propagation are calibrated according to the current attraction of the community to the nodes, which effectively improves the situation of misclassification in the early community detection. Finally, overlapping community detection is carried out based on the non-overlapping community structure. At this time, the overlapping community detection result is more accurate and interpretable. The community detection results on 3 synthetic networks and 12 real datasets show that the proposed algorithm has more advantages than four non-overlapping community detection methods and two overlapping community detection methods.