Is Objective Function the Silver Bullet? A Case Study of Community Detection Algorithms on Social Networks

2011 ◽  
Author(s):  
Yang Yang ◽  
Yizhou Sun ◽  
Saurav Pandit ◽  
Nitesh V. Chawla ◽  
Jiawei Han
Keyword(s):  
Social Networks ◽  
Objective Function ◽  
Community Detection ◽  
Detection Algorithms ◽  
Silver Bullet

scholarly journals Optimizing community detection in social networks using antlion and K-median

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Amany A. Naem ◽  
Neveen I. Ghali
Keyword(s):  
Social Networks ◽  
Objective Function ◽  
Community Detection ◽  
Optimization Problem ◽  
Real Life ◽  
Optimization Methods ◽  
Population Based ◽  
Detection Problem ◽  
Network Communities ◽  
Antlion Optimization

Antlion Optimization (ALO) is one of the latest population based optimization methods that proved its good performance in a variety of applications. The ALO algorithm copies the hunting mechanism of antlions to ants in nature. Community detection in social networks is conclusive to understanding the concepts of the networks. Identifying network communities can be viewed as a problem of clustering a set of nodes into communities. k-median clustering is one of the popular techniques that has been applied in clustering. The problem of clustering network can be formalized as an optimization problem where a qualitatively objective function that captures the intuition of a cluster as a set of nodes with better in ternal connectivity than external connectivity is selected to be optimized. In this paper, a mixture antlion optimization and k-median for solving the community detection problem is proposed and named as K-median Modularity ALO. Experimental results which are applied on real life networks show the ability of the mixture antlion optimization and k-median to detect successfully an optimized community structure based on putting the modularity as an objective function.

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.

COMMUNITY DETECTION IN SOCIAL NETWORKS EMPLOYING COMPONENT INDEPENDENCY

2009 ◽  
Vol 23 (17) ◽  
pp. 2089-2106 ◽  
Author(s):  
ZHONGMIN XIONG ◽  
WEI WANG
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Theoretical Analysis ◽  
Community Detection ◽  
Biological Networks ◽  
Real Data ◽  
Important Task ◽  
Underlying Structure ◽  
Data Sets ◽  
Detection Algorithms

Many networks, including social and biological networks, are naturally divided into communities. Community detection is an important task when discovering the underlying structure in networks. GN algorithm is one of the most influential detection algorithms based on betweenness scores of edges, but it is computationally costly, as all betweenness scores need to be repeatedly computed once an edge is removed. This paper presents an algorithm which is also based on betweenness scores but more than one edge can be removed when all betweenness scores have been computed. This method is motivated by the following considerations: many components, divided from networks, are independent of each other in their recalculation of betweenness scores and their split into smaller components. It is shown that this method is fast and effective through theoretical analysis and experiments with several real data sets, which have acted as test beds in many related works. Moreover, the version of this method with the minor adjustments allows for the discovery of the communities surrounding a given node without having to compute the full community structure of a graph.

A Comparative Study of Clustering Algorithms

2011 ◽  
Vol 3 (3) ◽  
pp. 1-18
Author(s):  
Kanna AlFalahi ◽  
Saad Harous ◽  
Yacine Atif
Keyword(s):  
Social Networks ◽  
Comparative Study ◽  
Collaborative Filtering ◽  
Community Detection ◽  
Clustering Algorithms ◽  
Social Web ◽  
Web Data ◽  
Huge Data ◽  
Multiple Algorithms

Clustering is a major problem when dealing with organizing and dividing data. There are multiple algorithms proposed to handle this issue in many scientific areas such as classifications, community detection and collaborative filtering. The need for clustering arises in Social Networks where huge data generated daily and different relations are established between users. The ability to find groups of interest in a network can help in many aspects to provide different services such as targeted advertisements. The authors surveyed different clustering algorithms from three different clustering groups: Hierarchical, Partitional, and Density-based algorithms. They then discuss and compare these algorithms from social web point view and show their strength and weaknesses in handling social web data. They also use a case study to support our finding by applying two clustering algorithms on articles collected from Delicious.com and discussing the different groups generated by each algorithm.

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.

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