Ego Based Community Detection in Online Social Network

2017 ◽  
pp. 205-210 ◽  
Author(s):  
Paramita Dey ◽  
Sarbani Roy ◽  
Sanjit Roy
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Online Social Network

Characterization of Online Social Network

2019 ◽  
pp. 272-299 ◽  
Author(s):  
Paramita Dey
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Online Social Network ◽  
Social Network Sites ◽  
Detection Algorithm ◽  
Centrality Measures ◽  
Large Network ◽  
Twitter Account ◽  
Community Detection Algorithm

The rapid growth of internet with large number of social network sites makes it easy to interconnect people from all over the world on a shared platform. Social network can be represented by a graph, where individual users are represented as nodes/vertices and connections between them are represented as edges of the graph. As social network inherits the properties of graph, its characterization includes centrality and community detection. In this chapter we discuss three centrality measures and its effects for information propagation. We discuss three popular hierarchical community detection measures and make a comparative analysis of them. Moreover we propose a new ego-based community detection algorithm which can be very efficient in terms of time complexity for very large network like online social network. In this chapter, a network is formed based on the data collected from Twitter account using hashtag(#).

scholarly journals Research on Community Detection of Online Social Network Members Based on the Sparse Subspace Clustering Approach

2019 ◽  
Vol 11 (12) ◽  
pp. 254
Author(s):  
Zihe Zhou ◽  
Bo Tian
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Online Social Network ◽  
Subspace Clustering ◽  
High Dimensional ◽  
Data Sets ◽  
Text Data ◽  
New Community

The text data of the social network platforms take the form of short texts, and the massive text data have high-dimensional and sparse characteristics, which does not make the traditional clustering algorithm perform well. In this paper, a new community detection method based on the sparse subspace clustering (SSC) algorithm is proposed to deal with the problem of sparsity and the high-dimensional characteristic of short texts in online social networks. The main ideal is as follows. First, the structured data including users’ attributions and user behavior and unstructured data such as user reviews are used to construct the vector space for the network. And the similarity of the feature words is calculated by the location relation of the feature words in the synonym word forest. Then, the dimensions of data are deduced based on the principal component analysis in order to improve the clustering accuracy. Further, a new community detection method of social network members based on the SSC is proposed. Finally, experiments on several data sets are performed and compared with the K-means clustering algorithm. Experimental results show that proper dimension reduction for high dimensional data can improve the clustering accuracy and efficiency of the SSC approach. The proposed method can achieve suitable community partition effect on online social network data sets.

Ensemble method: Community detection based on game theory

2014 ◽  
Vol 28 (30) ◽  
pp. 1450211 ◽  
Author(s):  
Xia Zhang ◽  
Zhengyou Xia ◽  
Shengwu Xu ◽  
J. D. Wang
Keyword(s):  
Game Theory ◽  
Social Network ◽  
Community Detection ◽  
Online Social Network ◽  
Cost Effective ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Model ◽  
Model Based ◽  
Active Research

Timely and cost-effective analytics over social network has emerged as a key ingredient for success in many businesses and government endeavors. Community detection is an active research area of relevance to analyze online social network. The problem of selecting a particular community detection algorithm is crucial if the aim is to unveil the community structure of a network. The choice of a given methodology could affect the outcome of the experiments because different algorithms have different advantages and depend on tuning specific parameters. In this paper, we propose a community division model based on the notion of game theory, which can combine advantages of previous algorithms effectively to get a better community classification result. By making experiments on some standard dataset, it verifies that our community detection model based on game theory is valid and better.

A fast parallel modularity optimization algorithm (FPMQA) for community detection in online social network

2013 ◽  
Vol 50 ◽  
pp. 246-259 ◽  
Author(s):  
Zhan Bu ◽  
Chengcui Zhang ◽  
Zhengyou Xia ◽  
Jiandong Wang
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Online Social Network

Characteristic analysis of information propagation pattern in online social network

2013 ◽  
Vol 33 (1) ◽  
pp. 105-107 ◽  
Author(s):  
Jia HAN ◽  
Ruliang XIAO ◽  
Yao HU ◽  
Tao TANG ◽  
Lina FANG
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Information Propagation ◽  
Characteristic Analysis ◽  
Propagation Pattern

Research on community detection in social network

2014 ◽  
Vol 31 (1) ◽  
pp. 35
Author(s):  
Na Wang ◽  
Xia Li ◽  
Hongying Xu
Keyword(s):  
Social Network ◽  
Community Detection
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