  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 $\rho$ and the minimum dissimi-larity $\delta$, are first
defined for each node in the graph. Then the nodes with both higher $\rho$ and
$\delta$ 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.
