Instability of clustering metrics in overlapping community detection algorithms

2021 ◽  
Author(s):  
Diego Kiedanski ◽  
Pablo Rodriguez-Bocca
Keyword(s):  
Community Detection ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Overlapping Community

Aspects of Various Community Detection Algorithms in Social Network Analysis

2019 ◽  
pp. 327-341
Author(s):  
Nicole Belinda Dillen ◽  
Aruna Chakraborty
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Novel Approach ◽  
Recent Developments ◽  
Overlapping Community ◽  
Related Individuals

One of the most important aspects of social network analysis is community detection, which is used to categorize related individuals in a social network into groups or communities. The approach is quite similar to graph partitioning, and in fact, most detection algorithms rely on concepts from graph theory and sociology. The aim of this chapter is to aid a novice in the field of community detection by providing a wider perspective on some of the different detection algorithms available, including the more recent developments in this field. Five popular algorithms have been studied and explained, and a recent novel approach that was proposed by the authors has also been included. The chapter concludes by highlighting areas suitable for further research, specifically targeting overlapping community detection algorithms.

Community Detection in Large-Scale Social Networks

2018 ◽  
pp. 189-206 ◽  
Author(s):  
S Rao Chintalapudi ◽  
M. H. M. Krishna Prasad
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Large Scale ◽  
The Other ◽  
Edge Density ◽  
Challenging Problem ◽  
Overlapping Community Detection ◽  
Clustering Problem ◽  
Detection Algorithms ◽  
Overlapping Community

Community Structure is one of the most important properties of social networks. Detecting such structures is a challenging problem in the area of social network analysis. Community is a collection of nodes with dense connections than with the rest of the network. It is similar to clustering problem in which intra cluster edge density is more than the inter cluster edge density. Community detection algorithms are of two categories, one is disjoint community detection, in which a node can be a member of only one community at most, and the other is overlapping community detection, in which a node can be a member of more than one community. This chapter reviews the state-of-the-art disjoint and overlapping community detection algorithms. Also, the measures needed to evaluate a disjoint and overlapping community detection algorithms are discussed in detail.

scholarly journals Evaluating the role of community detection in improving influence maximization heuristics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
László Hajdu ◽  
Miklós Krész ◽  
András Bóta
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Influence Maximization ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Overlapping Community ◽  
Community Detection Algorithm ◽  
The Cost

AbstractBoth community detection and influence maximization are well-researched fields of network science. Here, we investigate how several popular community detection algorithms can be used as part of a heuristic approach to influence maximization. The heuristic is based on the community value, a node-based metric defined on the outputs of overlapping community detection algorithms. This metric is used to select nodes as high influence candidates for expanding the set of influential nodes. Our aim in this paper is twofold. First, we evaluate the performance of eight frequently used overlapping community detection algorithms on this specific task to show how much improvement can be gained compared to the originally proposed method of Kempe et al. Second, selecting the community detection algorithm(s) with the best performance, we propose a variant of the influence maximization heuristic with significantly reduced runtime, at the cost of slightly reduced quality of the output. We use both artificial benchmarks and real-life networks to evaluate the performance of our approach.

A Survey on Overlapping Communities in Large-Scale Social Networks

2018 ◽  
pp. 198-215
Author(s):  
S Rao Chintalapudi ◽  
H. M. Krishna Prasad M
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Biological Networks ◽  
Modern World ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Detection Algorithms ◽  
Research Areas ◽  
Network Generator ◽  
Overlapping Community

Social network analysis is one of the emerging research areas in the modern world. Social networks can be adapted to all the sectors by using graph theory concepts such as transportation networks, collaboration networks, and biological networks and so on. The most important property of social networks is community, collection of nodes with dense connections inside and sparse connections at outside. Community detection is similar to clustering analysis and has many applications in the real-time world such as recommendation systems, target marketing and so on. Community detection algorithms are broadly classified into two categories. One is disjoint community detection algorithms and the other is overlapping community detection algorithms. This chapter reviews overlapping community detection algorithms with their strengths and limitations. To evaluate these algorithms, a popular synthetic network generator, i.e., LFR benchmark generator and the new extended quality measures are discussed in detail.

scholarly journals A Semantic Community Detection Algorithm Based on Quantizing Progress

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Xu Han ◽  
Deyun Chen ◽  
Hailu Yang
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Semantic Information ◽  
Semantic Space ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Overlapping Community ◽  
Community Detection Algorithm ◽  
The Ideal

The semantic social network is a kind of network that contains enormous nodes and complex semantic information, and the traditional community detection algorithms could not give the ideal cogent communities instead. To solve the issue of detecting semantic social network, we present a clustering community detection algorithm based on the PSO-LDA model. As the semantic model is LDA model, we use the Gibbs sampling method that can make quantitative parameters map from semantic information to semantic space. Then, we present a PSO strategy with the semantic relation to solve the overlapping community detection. Finally, we establish semantic modularity (SimQ) for evaluating the detected semantic communities. The validity and feasibility of the PSO-LDA model and the semantic modularity are verified by experimental analysis.

Aspects of Various Community Detection Algorithms in Social Network Analysis

2018 ◽  
pp. 6961-6972
Author(s):  
Nicole Belinda Dillen ◽  
Aruna Chakraborty
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Novel Approach ◽  
Recent Developments ◽  
Overlapping Community ◽  
Related Individuals

One of the most important aspects of social network analysis is community detection, which is used to categorize related individuals in a social network into groups or communities. The approach is quite similar to graph partitioning and, in fact, most detection algorithms rely on concepts from graph theory and sociology. The aim of this chapter is to aid a novice in the field of community detection by providing a wider perspective on some of the different detection algorithms available, including the more recent developments in this field. Five popular algorithms have been studied and explained, and a recent novel approach that was proposed by the authors has also been included. The chapter concludes by highlighting areas suitable for further research, specifically targeting overlapping community detection algorithms.

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