Evidential Label Propagation Algorithm for Graphs

TL;DR

The paper introduces Evidential Label Propagation (ELP), an advanced community detection algorithm for large graphs that improves accuracy and robustness by integrating belief functions, node influence, and plausibility measures.

Contribution

It extends the traditional Label Propagation Algorithm by incorporating belief functions, node influence, and plausibility, enabling detection of overlapping nodes and outliers.

Findings

ELP converges efficiently on large networks.

ELP outperforms traditional LPA in accuracy.

ELP detects overlapping communities and outliers.

Abstract

Community detection has attracted considerable attention crossing many areas as it can be used for discovering the structure and features of complex networks. With the increasing size of social networks in real world, community detection approaches should be fast and accurate. The Label Propagation Algorithm (LPA) is known to be one of the near-linear solutions and benefits of easy implementation, thus it forms a good basis for efficient community detection methods. In this paper, we extend the update rule and propagation criterion of LPA in the framework of belief functions. A new community detection approach, called Evidential Label Propagation (ELP), is proposed as an enhanced version of conventional LPA. The node influence is first defined to guide the propagation process. The plausibility is used to determine the domain label of each node. The update order of nodes is discussed to improve the robustness of the method. ELP algorithm will converge after the domain labels of all the nodes become unchanged. The mass assignments are calculated finally as memberships of nodes. The overlapping nodes and outliers can be detected simultaneously through the proposed method. The experimental results demonstrate the effectiveness of ELP.