Cohesive Subgraph Models for Overlapping Community Search over Networks

2020 ◽  
Author(s):  
Khaled Adeyl ◽  
Mourad Kmimech ◽  
Nizar Mhadhbi ◽  
Badran Raddaoui
Keyword(s):  
Complex Networks ◽  
Undirected Graph ◽  
Challenging Problem ◽  
Unified Framework ◽  
Large Networks ◽  
Empirical Results ◽  
Community Search ◽  
Significant Interest ◽  
Overlapping Community ◽  
Effective Models

There has been significant interest in the study of the problem of community search in large networks. Given one or more query nodes, this problem aims to discover densely connected subgroups containing these nodes. Various algorithms have been proposed to solve this challenging problem using different measures or a variety of cohesive subgraphs. In this paper, given an undirected graph and a set of query nodes, we study the community search using novel several cohesive subgraph models. More precisely, we propose to exploit several cohesive structures in a unified framework to find densely communities for query nodes in large complex networks. First, we review some existing cohesive structures. Next, to make these structures more effective models of communities, we focus on interesting configurations that are larger and more cohesive by fulfilling some constraints. The new structures obtained allow to ensure a larger density on the discovered communities and overcome some weaknesses of existing models. Finally, empirical results show the effectiveness of our framework to find communities for query nodes in a variety of real graphs.

scholarly journals Visualizing real-world networks

2021 ◽  
Author(s):  
Lyndsay Roach
Keyword(s):  
Complex Networks ◽  
Real World ◽  
The Internet ◽  
Network Data ◽  
Community Structures ◽  
Large Networks ◽  
Computing Power ◽  
On Line ◽  
Using Data ◽  
The Impact

The study of networks has been propelled by improvements in computing power, enabling our ability to mine and store large amounts of network data. Moreover, the ubiquity of the internet has afforded us access to records of interactions that have previously been invisible. We are now able to study complex networks with anywhere from hundreds to billions of nodes; however, it is difficult to visualize large networks in a meaningful way. We explore the process of visualizing real-world networks. We first discuss the properties of complex networks and the mechanisms used in the network visualizing software Gephi. Then we provide examples of voting, trade, and linguistic networks using data extracted from on-line sources. We investigate the impact of hidden community structures on the analysis of these real-world networks.

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.

Detecting Overlapping Communities with MDS and Local Expansion FCM

2014 ◽  
Vol 644-650 ◽  
pp. 3295-3299
Author(s):  
Lin Li ◽  
Zheng Min Xia ◽  
Sheng Hong Li ◽  
Li Pan ◽  
Zhi Hua Huang
Keyword(s):  
Community Structure ◽  
Euclidean Space ◽  
Complex Networks ◽  
Test Results ◽  
Distance Information ◽  
Overlapping Communities ◽  
Expansion Strategy ◽  
Local Expansion ◽  
Structural And Functional Properties ◽  
Overlapping Community

Community structure is an important feature to understand structural and functional properties in various complex networks. In this paper, we use Multidimensional Scaling (MDS) to map nodes of network into Euclidean space to keep the distance information of nodes, and then we use topology feature of communities to propose the local expansion strategy to detect initial seeds for FCM. Finally, the FCM are used to uncover overlapping communities in the complex networks. The test results in real-world and artificial networks show that the proposed algorithm is efficient and robust in uncovering overlapping community structure.

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