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Graph communities in Neo4j
- Source :
- Evolving Systems. 11:397-407
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- Community discovery is an essential topic in social network analysis since it provides a way for recursively decomposing a large social graph to easily interpretable subgraphs. The implementation of four major community discovery algorithms, namely the Newman–Girvan or Edge Betweeness, the Walktrap, the Louvain, and the CNM as Java analytics over Neo4j is described. Their correctness was evaluated functionally in two real Twitter graphs with vastly different characteristics. This was done on the grounds that a successful structural graph partitioning should eventually be reflected in the network functionality domain. Additionally, most real world graphs lack a list of ground truth communities, rendering a structural verification difficult, while functionality can be easily observed in most cases. Naturally, this renders the evaluation network-specific, as different social networks have different operational characteristics. The primary algorithmic finding was that the Louvain algorithm yields Twitter communities whose distribution size matches closer, in terms of the Kullback–Leibler divergence, the tweet and retweet distributions, with Newman–Girvan, Walktrap, and CNM following in that order.
- Subjects :
- Social graph
Ground truth
Control and Optimization
Correctness
Graph database
Theoretical computer science
Java
business.industry
Computer science
Graph partition
02 engineering and technology
computer.software_genre
030218 nuclear medicine & medical imaging
Computer Science Applications
Rendering (computer graphics)
03 medical and health sciences
0302 clinical medicine
Control and Systems Engineering
Analytics
Modeling and Simulation
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
business
computer
computer.programming_language
Subjects
Details
- ISSN :
- 18686486 and 18686478
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Evolving Systems
- Accession number :
- edsair.doi...........95dcad4fc76cf9d5db40bcef5e85008c
- Full Text :
- https://doi.org/10.1007/s12530-018-9244-x