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Constrained Spectral Clustering for Dynamic Community Detection
- Source :
- ICASSP
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Networks are useful representations of many systems with interacting entities, such as social, biological and physical systems. Characterizing the meso-scale organization, i.e. the community structure, is an important problem in network science. Community detection aims to partition the network into sets of nodes that are densely connected internally but sparsely connected to other dense sets of nodes. Current work on community detection mostly focuses on static networks. However, many real world networks are dynamic, i.e. their structure and properties change with time, requiring methods for dynamic community detection. In this paper, we propose a new stochastic block model (SBM) for modeling the evolution of community membership. Unlike existing SBMs, the proposed model allows each community to evolve at a different rate. This new model is used to derive a maximum a posteriori estimator for community detection, which can be written as a constrained spectral clustering problem. In particular, the transition probabilities for each community modify the graph adjacency matrix at each time point. This formulation provides a relationship between statistical network inference and spectral clustering for dynamic networks. The proposed method is evaluated on both simulated and real dynamic networks.
- Subjects :
- Social and Information Networks (cs.SI)
FOS: Computer and information sciences
Physics - Physics and Society
Theoretical computer science
Computer science
Community structure
FOS: Physical sciences
Inference
Computer Science - Social and Information Networks
Network science
Physics and Society (physics.soc-ph)
01 natural sciences
Spectral clustering
Graph
010305 fluids & plasmas
Stochastic block model
0103 physical sciences
Graph (abstract data type)
Adjacency matrix
010306 general physics
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Accession number :
- edsair.doi.dedup.....df60ea432ca4de35615f3b09a53e8b84