Distribution-free model for community detection

Community detection for unweighted networks has been widely studied in network analysis, but the case of weighted networks remains a challenge. This paper proposes a general distribution-free model (DFM) for weighted networks in which nodes are partitioned into different communities. DFM can be seen...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Progress of theoretical and experimental physics Ročník 2023; číslo 3
Hlavný autor: Qing, Huan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Oxford University Press 01.03.2023
Predmet:
Algorithms
Clustering
Connectivity
Expected values
Normal distribution
ISSN:2050-3911, 2050-3911
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Community detection for unweighted networks has been widely studied in network analysis, but the case of weighted networks remains a challenge. This paper proposes a general distribution-free model (DFM) for weighted networks in which nodes are partitioned into different communities. DFM can be seen as a generalization of the famous stochastic block models from unweighted networks to weighted networks. DFM does not require prior knowledge of a specific distribution for elements of the adjacency matrix but only the expected value. In particular, signed networks with latent community structures can be modeled by DFM. We build a theoretical guarantee to show that a simple spectral clustering algorithm stably yields consistent community detection under DFM. We also propose a four-step data generation process to generate adjacency matrices with missing edges by combining DFM, noise matrix, and a model for unweighted networks. Using experiments with simulated and real datasets, we show that some benchmark algorithms can successfully recover community membership for weighted networks generated by the proposed data generation process.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2050-3911
2050-3911
DOI:10.1093/ptep/ptad024