arXiv:1612.01489
MCMC Louvain for Online Community Detection
원문 초록
We introduce a novel algorithm of community detection that maintains dynamically a community structure of a large network that evolves with time. The algorithm maximizes the modularity index thanks to the construction of a randomized hierarchical clustering based on a Monte Carlo Markov Chain (MCMC) method. Interestingly, it could be seen as a dynamization of Louvain algorithm (see Blondel et Al, 2008) where the aggregation step is replaced by the hierarchical instrumental probability.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Strongly Consistent Community Detection in Popularity Adjusted Block Models
Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm
On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models
Privacy-Preserving Transfer Learning for Community Detection using Locally Distributed Multiple Networks
MRAEA: An Efficient and Robust Entity Alignment Approach for Cross-lingual Knowledge Graph
Using deep learning for community discovery in social networks
토론
이 논문과 연결된 포럼 글이 아직 없습니다.