Evidential community detection based on density peaks
This work addresses the challenge of handling uncertainty in community detection for graph data, which is incremental as it builds on existing belief function frameworks.
The paper tackled the problem of detecting uncertain community structures in graph data by proposing a novel evidential community detection algorithm based on density peaks (EDPC), which uses local density and minimum dissimilarity metrics to identify centers and assigns memberships via belief functions, demonstrating effectiveness on real-world networks.
Credal partitions in the framework of belief functions can give us a better understanding of the analyzed data set. In order to find credal community structure in graph data sets, in this paper, we propose a novel evidential community detection algorithm based on density peaks (EDPC). Two new metrics, the local density $ρ$ and the minimum dissimi-larity $δ$, are first defined for each node in the graph. Then the nodes with both higher $ρ$ and $δ$ values are identified as community centers. Finally, the remaing nodes are assigned with corresponding community labels through a simple two-step evidential label propagation strategy. The membership of each node is described in the form of basic belief assignments , which can well express the uncertainty included in the community structure of the graph. The experiments demonstrate the effectiveness of the proposed method on real-world networks.