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 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.

scholarly journals Efficient Community Detection in Heterogeneous Social Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Zhen Li ◽  
Zhisong Pan ◽  
Yanyan Zhang ◽  
Guopeng Li ◽  
Guyu Hu
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Community Detection ◽  
Social Relations ◽  
Real World ◽  
User Generated Content ◽  
Detection Accuracy ◽  
The Common ◽  
Heterogeneous Social Networks ◽  
Content Information

Community detection is of great importance which enables us to understand the network structure and promotes many real-world applications such as recommendation systems. The heterogeneous social networks, which contain multiple social relations and various user generated content, make the community detection problem more complicated. Particularly, social relations and user generated content are regarded as link information and content information, respectively. Since the two types of information indicate a common community structure from different perspectives, it is better to mine them jointly to improve the detection accuracy. Some detection algorithms utilizing both link and content information have been developed. However, most works take the private community structure of a single data source as the common one, and some methods take extra time transforming the content data into link data compared with mining directly. In this paper, we propose a framework based on regularized joint nonnegative matrix factorization (RJNMF) to utilize link and content information jointly to enhance the community detection accuracy. In the framework, we develop joint NMF to analyze link and content information simultaneously and introduce regularization to obtain the common community structure directly. Experimental results on real-world datasets show the effectiveness of our method.

scholarly journals Dynamic Community Structure in Online Social Groups

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 113
Author(s):  
Barbara Guidi ◽  
Andrea Michienzi
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Online Social Networks ◽  
Social Groups ◽  
Vast Number ◽  
Detection Algorithms ◽  
Human Scale ◽  
Online Social Media ◽  
Dynamic Community Detection ◽  
Dynamic Community

One of the main ideas about the Internet is to rethink its services in a user-centric fashion. This fact translates to having human-scale services with devices that will become smarter and make decisions in place of their respective owners. Online Social Networks and, in particular, Online Social Groups, such as Facebook Groups, will be at the epicentre of this revolution because of their great relevance in the current society. Despite the vast number of studies on human behaviour in Online Social Media, the characteristics of Online Social Groups are still unknown. In this paper, we propose a dynamic community detection driven study of the structure of users inside Facebook Groups. The communities are extracted considering the interactions among the members of a group and it aims at searching dense communication groups of users, and the evolution of the communication groups over time, in order to discover social properties of Online Social Groups. The analysis is carried out considering the activity of 17 Facebook Groups, using 8 community detection algorithms and considering 2 possible interaction lifespans. Results show that interaction communities in OSGs are very fragmented but community detection tools are capable of uncovering relevant structures. The study of the community quality gives important insights about the community structure and increasing the interaction lifespan does not necessarily result in more clusterized or bigger communities.

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.

scholarly journals A New Algorithm to Detect and Evaluate Learning Communities in Social Networks: Facebook Groups

2019 ◽  
Vol 14 (23) ◽  
pp. 165
Author(s):  
Meriem Adraoui ◽  
Asmaâ Retbi ◽  
Mohammed Khalidi Idrissi ◽  
Samir Bennani
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Learning Communities ◽  
Community Detection ◽  
New Method ◽  
Second Phase ◽  
Community Learning ◽  
Detection Algorithms ◽  
Two Phases ◽  
Set Up

This article aims to present a new method of evaluating learners by communities on Facebook groups which based on their interactions. The objective of our study is to set up a community learning structure according to the learners' levels. In this context, we have proposed a new algorithm to detect and evaluate learning communities. Our algorithm consists of two phases. The first phase aims to evaluate learners by measuring their degrees of ‘Safely’. The second phase is used to detect communities. These two phases will be repeated until the best community structure is found. Finally, we test the performance of our proposed approach on five Facebook groups. Our algorithm gives good results compared to other community detection algorithms.

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