An Analysis and Comparison of Community Detection Algorithms in Online Social Networks

2021 ◽  
pp. 363-370
Author(s):  
Sanjeev Dhawan ◽  
Kulvinder Singh ◽  
Amit Batra
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Online Social Networks ◽  
Detection Algorithms

scholarly journals Efficient Estimation of Network Games of Incomplete Information: Application to Large Online Social Networks

2021 ◽  
Author(s):  
Xi Chen ◽  
Ralf van der Lans ◽  
Michael Trusov
Keyword(s):  
Social Networks ◽  
Social Influence ◽  
Incomplete Information ◽  
Community Detection ◽  
Online Social Networks ◽  
Choice Model ◽  
Real Life ◽  
Efficient Estimation ◽  
Data Set ◽  
Detection Algorithms

This paper presents a structural discrete choice model with social influence for large-scale social networks. The model is based on an incomplete information game and permits individual-specific parameters of consumers. It is challenging to apply this type of models to real-life scenarios for two reasons: (1) The computation of the Bayesian–Nash equilibrium is highly demanding; and (2) the identification of social influence requires the use of excluded variables that are oftentimes unavailable. To address these challenges, we derive the unique equilibrium conditions of the game, which allow us to employ a stochastic Bayesian estimation procedure that is scalable to large social networks. To facilitate the identification, we utilize community-detection algorithms to divide the network into different groups that, in turn, can be used to construct excluded variables. We validate the proposed structural model with the login decisions of more than 25,000 users of an online social game. Importantly, this data set also contains promotions that were exogenously determined and targeted to only a subgroup of consumers. This information allows us to perform exogeneity tests to validate our identification strategy using community-detection algorithms. Finally, we demonstrate the managerial usefulness of the proposed methodology for improving the strategies of targeting influential consumers in large social networks. This paper was accepted by Matthew Shum, marketing.

scholarly journals Community Detection Using Node Attributes and Structural Patterns in Online Social Networks

2017 ◽  
Vol 10 (4) ◽  
pp. 50
Author(s):  
Bikash Chandra Singh ◽  
Mohammad Muntasir Rahman ◽  
Md Sipon Miah ◽  
Mrinal Kanti Baowaly
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Online Social Networks ◽  
Online Social Network ◽  
Detection Methods ◽  
Structural Patterns ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Node Attributes ◽  
Overlapping Community

Community detection in online social networks is a difficult but important phenomenon in term of revealing hidden relationships patterns among people so that we can understand human behaviors in term of social-economics perspectives. Community detection algorithms allow us to discover these types of patterns in online social networks. Identifying and detecting communities are not only of particular importance but also have immediate applications. For this reason, researchers have been intensively investigated to implement efficient algorithms to detect community in recent years. In this paper, we introduce set theory to address the community detection problem considering node attributes and network structural patterns. We also formulate probability theory to detect the overlapping community in online social network. Furthermore, we extend our focus on the comparative analysis on some existing community detection methods, which basically consider node attributes and edge contents for detecting community. We conduct comprehensive analysis on our framework so that we justify the performance of our proposed model. The experimental results show the effectiveness of the proposed approach.

A Survey of Parallel Community Detection Algorithms

2017 ◽  
pp. 1-26
Author(s):  
Sobin C. C. ◽  
Vaskar Raychoudhury ◽  
Snehanshu Saha
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Community Structure ◽  
Community Detection ◽  
Social Relations ◽  
Online Social Networks ◽  
Detection Algorithms ◽  
Big Data Applications ◽  
Comprehensive Survey ◽  
External Connections

The amount of data generated by online social networks such as Facebook, Twitter, etc., has recently experienced an enormous growth. Extracting useful information such as community structure, from such large networks is very important in many applications. Community is a collection of nodes, having dense internal connections and sparse external connections. Community detection algorithms aim to group nodes into different communities by extracting similarities and social relations between nodes. Although, many community detection algorithms in literature, they are not scalable enough to handle large volumes of data generated by many of the today's big data applications. So, researchers are focusing on developing parallel community detection algorithms, which can handle networks consisting of millions of edges and vertices. In this article, we present a comprehensive survey of parallel community detection algorithms, which is the first ever survey in this domain, although, multiple papers exist in literature related to sequential 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.

Community Detection Approaches in Real World Networks

2014 ◽  
Vol 6 (1) ◽  
pp. 35-51 ◽  
Author(s):  
Pooja Wadhwa ◽  
M.P.S Bhatia
Keyword(s):  
Community Detection ◽  
Online Social Networks ◽  
Temporal Dynamics ◽  
Detection Methods ◽  
Unified Approach ◽  
Detection Algorithms ◽  
Dynamic Community Detection ◽  
Dynamic Communities ◽  
Dynamic Community

Online social networks have been continuously evolving and one of their prominent features is the evolution of communities which can be characterized as a group of people who share a common relationship among themselves. Earlier studies on social network analysis focused on static network structures rather than dynamic processes, however, with the passage of time, the networks have also evolved and the researchers have started to focus on the aspect of studying dynamic behavior of networks. This paper aims to present an overview of community detection approaches graduating from static community detection methods towards the methods to identify dynamic communities in networks. The authors also present a classification of the existing dynamic community detection algorithms along the dimension of studying the evolution as either a two-step approach comprising of community detection via static methods and then applying temporal dynamics or a unified approach which comprises of dynamic detection of communities along with their evolutionary characteristics.

Community Detection in Online Social Networks: A Differentially Private and Parsimonious Approach

2020 ◽  
Vol 7 (1) ◽  
pp. 151-163 ◽  
Author(s):  
Tianxi Ji ◽  
Changqing Luo ◽  
Yifan Guo ◽  
Qianlong Wang ◽  
Lixing Yu ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Online Social Networks

Community detection in social networks

2017 ◽  
Vol 01 (01) ◽  
pp. 1630001 ◽  
Author(s):  
Hossein Fani ◽  
Ebrahim Bagheri
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Computer Science ◽  
Community Detection ◽  
Online Social Networks ◽  
Online Community ◽  
Detection Methods ◽  
Fundamental Part ◽  
Social Settings ◽  
Online Experience

Online social networks have become a fundamental part of the global online experience. They facilitate different modes of communication and social interactions, enabling individuals to play social roles that they regularly undertake in real social settings. In spite of the heterogeneity of the users and interactions, these networks exhibit common properties. For instance, individuals tend to associate with others who share similar interests, a tendency often known as homophily, leading to the formation of communities. This entry aims to provide an overview of the definitions for an online community and review different community detection methods in social networks. Finding communities are beneficial since they provide summarization of network structure, highlighting the main properties of the network. Moreover, it has applications in sociology, biology, marketing and computer science which help scientists identify and extract actionable insight.

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