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(#).

Consumer perceptions of fresh leafy vegetables in Japan

2018 ◽  
Vol 120 (11) ◽  
pp. 2554-2568
Author(s):  
Yuki Yano ◽  
David Blandford ◽  
Atsushi Maruyama ◽  
Tetsuya Nakamura
Keyword(s):  
Community Detection ◽  
Detection Algorithm ◽  
Consumer Perceptions ◽  
Leafy Vegetables ◽  
Centrality Measures ◽  
Bulletin Board ◽  
Content Type ◽  
Consumer Responses ◽  
Online Bulletin Board ◽  
Community Detection Algorithm

Purpose The purpose of this paper is to investigate Japanese consumer perceptions of the benefits of consuming fresh leafy vegetables. Design/methodology/approach An online bulletin board survey was conducted in Japan to collect responses to an open-ended question about reasons for consuming fresh leafy vegetables. A total of 897 responses were analysed using word co-occurrence network analysis. A community detection method and centrality measures were used to interpret the resulting network map. Findings Using a community detection algorithm, the authors identify six major groups of words that represent respondents’ core motives for consuming leafy vegetables. While Japanese consumers view health benefits to be most important, sensory factors, such as texture, colour, and palatability, and convenience factors also influence attitudes. The authors find that centrality measures can be useful in identifying keywords that appear in various contexts of consumer responses. Originality/value This is the first paper to use a quantitative text analysis to examine consumer perceptions for fresh leafy vegetables. The analysis also provides pointers for creating visually interpretable co-occurrence network maps from textual data and discusses the role of community structure and centrality in interpreting such maps.

Stable Communities Detection Method for Temporal Multiplex Graphs: Heterogeneous Social Network Case Study

2020 ◽  
Author(s):  
Wala Rebhi ◽  
Nesrine Ben Yahia ◽  
Narjès Bellamine Ben Saoud
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Step Method ◽  
Temporal Dimension ◽  
Temporal Aspect ◽  
Heterogeneous Social Networks ◽  
Community Detection Algorithm ◽  
High Level ◽  
Temporal Graphs

Abstract Multiplex graphs have been recently proposed as a model to represent high-level complexity in real-world networks such as heterogeneous social networks where actors could be characterized by heterogeneous properties and could be linked with different types of social interactions. This has brought new challenges in community detection, which aims to identify pertinent groups of nodes in a complex graph. In this context, great efforts have been made to tackle the problem of community detection in multiplex graphs. However, most of the proposed methods until recently deal with static multiplex graph and ignore the temporal dimension, which is a key characteristic of real networks. Even more, the few methods that consider temporal graphs, they just propose to follow communities over time and none of them use the temporal aspect directly to detect stable communities, which are often more meaningful in reality. Thus, this paper proposes a new two-step method to detect stable communities in temporal multiplex graphs. The first step aims to find the best static graph partition at each instant by applying a new hybrid community detection algorithm, which considers both relations heterogeneities and nodes similarities. Then, the second step considers the temporal dimension in order to find final stable communities. Finally, experiments on synthetic graphs and a real social network show that this method is competitive and it is able to extract high-quality communities.

scholarly journals Multilayer Social Network Overlapping Community Detection Algorithm Based on Trust Relationship

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Junjie Jia ◽  
Pengtao Liu ◽  
Xiaojin Du ◽  
Yuchao Zhang
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Experimental Comparison ◽  
Overlapping Community Detection ◽  
Trust Value ◽  
Overlapping Communities ◽  
Trust Relationship ◽  
Overlapping Community ◽  
Community Detection Algorithm

Aiming at the problem of the lack of user social attribute characteristics in the process of dividing overlapping communities in multilayer social networks, in this paper, we propose a multilayer social network overlapping community detection algorithm based on trust relationship. By combining structural trust and social attribute trust, we transform a complex multilayer social network into a single-layer trust network. We obtain the community structure according to the community discovery algorithm based on trust value and merge communities with higher overlap. The experimental comparison and analysis are carried out on the synthetic network and the real network, respectively. The experimental results show that the proposed algorithm has higher harmonic mean and modularity than other algorithms of the same type.

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.

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.

scholarly journals Overlapping Community Detection Based on Node Importance and Adjacency Information

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ping Wang ◽  
Yonghong Huang ◽  
Fei Tang ◽  
Hongtao Liu ◽  
Yangyang Lu
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Community Detection ◽  
Evaluation Method ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Node Importance ◽  
Overlapping Community ◽  
Community Detection Algorithm

Detecting the community structure and predicting the change of community structure is an important research topic in social network research. Focusing on the importance of nodes and the importance of their neighbors and the adjacency information, this article proposes a new evaluation method of node importance. The proposed overlapping community detection algorithm (ILE) uses the random walk to select the initial community and adopts the adaptive function to expand the community. It finally optimizes the community to obtain the overlapping community. For the overlapping communities, this article analyzes the evolution of networks at different times according to the stability and differences of social networks. Seven common community evolution events are obtained. The experimental results show that our algorithm is feasible and capable of discovering overlapping communities in complex social network efficiently.

Facebooking Alone? Millennials’ Use of Social Network Sites and Volunteering

2019 ◽  
Vol 49 (1) ◽  
pp. 203-217 ◽  
Author(s):  
Young-joo Lee
Keyword(s):  
Cohort Study ◽  
Social Network ◽  
Civic Engagement ◽  
Online Social Network ◽  
Social Network Sites ◽  
Positive Association ◽  
Prosocial Behaviors ◽  
Network Site ◽  
Facebook Use ◽  
Mixed Messages

The younger generation’s widespread use of online social network sites has raised concerns and debates about social network sites’ influence on this generation’s civic engagement, whether these sites undermine or promote prosocial behaviors. This study empirically examines how millennials’ social network site usage relates to volunteering, using the 2013 data of the Minnesota Adolescent Community Cohort Study. The findings reveal a positive association between a moderate level of Facebook use and volunteering, although heavy users are not more likely to volunteer than nonusers. This bell-shaped relationship between Facebook use and volunteering contrasts with the direct correlation between participation in off-line associational activities and volunteering. Overall, the findings suggest that it is natural to get mixed messages about social network sites’ impacts on civic engagement, and these platforms can be useful tools for getting the word out and recruiting episodic volunteers.

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