Degree and betweenness-based label propagation for community detection

Abstract

Community detection, as a crucial network analysis technique, holds significant application value in uncovering the underlying organizational structure in complex networks. In this paper, we propose a degree and betweenness-based label propagation method for community detection (DBLPA). First, we calculate the importance of each node by combining node degree and betweenness centrality. A node i is considered as a core node in the network if its importance is maximal among its neighbor nodes. Next, layer-by-layer label propagation starts from core nodes. The first layer of nodes for label propagation consists of the first-order neighbors of all core nodes. In the first layer of label propagation, the labels of core nodes are first propagated to the non-common neighbor nodes between core nodes, and then to the common neighbor nodes between core nodes. At the same time, the flag parameter is set to record the changing times of a node’s label, which is helpful to calibrate the node’s labels during the label propagation. It effectively improves the misclassification in the process of label propagation. We test the DBLPA on four real network datasets and nine synthetic network datasets, and the experimental results show that the DBLPA can effectively improve the accuracy of community detection.