15 Citations
Detecting community structure by belonging intensity analysis of intermediate nodes
- Computer ScienceInternational Journal of Modern Physics C
- 2019
Community structure is a common characteristic of complex networks and community detection is an important methodology to reveal the structure of real-world networks. In recent years, many algorithms…
New Community Estimation Method in Bipartite Networks Based on Quality of Filtering Coefficient
- Computer ScienceSci. Program.
- 2019
This paper proposes a method named as “biCNEQ” (bipartite network communities number estimation based on quality of filtering coefficient), which ensures that communities are all pure type, for estimating the number of communities in a bipartitenetwork.
Community detection using boundary nodes in complex networks
- Computer SciencePhysica A: Statistical Mechanics and its Applications
- 2019
Community Detection Method Based on Node Density, Degree Centrality, and K-Means Clustering in Complex Network
- Computer ScienceEntropy
- 2019
A community detection approach, the density-degree centrality-jaccard-k-means method (DDJKM), which can avoid the problem of random selection of initial cluster centers in conventional k-mean clustering algorithms, and is practical a approach for the detection of communities with large network datasets.
Neighbors-based divisive algorithm for hierarchical analysis in networks
- Computer ScienceInternational Journal of Modern Physics C
- 2019
Hierarchical analysis for network structure can point out which communities can constitute a larger group or give reasonable smaller groups within a community. Numerous methods for discovering…
A similarity based generalized modularity measure towards effective community discovery in complex networks
- Computer SciencePhysica A: Statistical Mechanics and its Applications
- 2019
A comprehensive literature review on community detection: Approaches and applications
- Computer ScienceANT/EDI40
- 2019
Community Detection via Local Learning Based on Generalized Metric With Neighboring Regularization
- Computer ScienceIEEE Transactions on Systems, Man, and Cybernetics: Systems
- 2022
This work proposes a novel community detection framework called LL-GMR, which is a local learning framework based on generalized metric with neighboring regularization, and consistently outperforms other state-of-the-art community detection approaches in terms of discovering ground-truth communities in six real-life networks.
A Novel Snowball-Chain Approach for Detecting Community Structures in Social Graphs
- Computer Science2019 IEEE Symposium Series on Computational Intelligence (SSCI)
- 2019
A novel community detection approach, termed as snowball-chain (SbChain), for identifying communities in social networks that follows a bottom-up approach to find the most prominent nodes based on the degree of overlapping neighbors and clustering coefficient, that may form some cliques.
48 References
Near linear time algorithm to detect community structures in large-scale networks.
- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2007
This paper investigates a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a predefined objective function nor prior information about the communities.
Community structure in social and biological networks
- Computer ScienceProceedings of the National Academy of Sciences of the United States of America
- 2002
This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Finding overlapping communities in networks by label propagation
- Computer ScienceArXiv
- 2009
The main contribution is to extend the label and propagation step to include information about more than one community: each vertex can now belong to up to v communities, where v is the parameter of the algorithm.
Mixing local and global information for community detection in large networks
- Computer ScienceJ. Comput. Syst. Sci.
- 2014
Finding Statistically Significant Communities in Networks
- Computer SciencePloS one
- 2011
OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics, is presented.
Enhancing community detection by using local structural information
- Computer ScienceArXiv
- 2016
The experimental results show that theLocal similarity measures are crucial for the improvement of community detection methods, while the positive effect of the local similarity measures is closely related to the networks under study and applied community Detection methods.
Defining and identifying communities in networks.
- Computer ScienceProceedings of the National Academy of Sciences of the United States of America
- 2004
This article proposes a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability and applies to a network of scientific collaborations, which, for its size, cannot be attacked with the usual methods.
Benchmark graphs for testing community detection algorithms.
- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2008
This work introduces a class of benchmark graphs, that account for the heterogeneity in the distributions of node degrees and of community sizes, and uses this benchmark to test two popular methods of community detection, modularity optimization, and Potts model clustering.