Fusion of Community Structures in Multiplex Networks by Label Constraints
Paper i proceeding, 2018

We develop a Belief Propagation algorithm for community detection problem in multiplex networks, which more accurately represents many real-world systems. Previous works have established that real world multiplex networks exhibit redundant structures/communities, and that community detection performance improves by aggregating (fusing) redundant layers which are generated from the same Stochastic Block Model (SBM). We introduce a probability model for generic multiplex networks, aiming to fuse community structure across layers, without assuming or seeking the same SBM generative model for different layers. Numerical experiment shows that our model finds out consistent communities between layers and yields a significant detectability improvement over the single layer architecture. Our model also achieves a comparable performance to a reference model where we assume consistent communities in prior. Finally we compare our method with multilayer modularity optimization in heterogeneous networks, and show that our method detects correct community labels more reliably.


Yuming Huang

North Carolina State University

Ashkan Panahi

North Carolina State University

Hamid Krim

North Carolina State University

Liyi Dai

U.S. Army Research Office

26th European Signal Processing Conference (EUSIPCO)


26th European Signal Processing Conference (EUSIPCO)
Rome, Italy,




Datavetenskap (datalogi)

Annan elektroteknik och elektronik



Mer information

Senast uppdaterat