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.

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 Identifying Communities in Dynamic Networks Using Information Dynamics

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 425
Author(s):  
Zejun Sun ◽  
Jinfang Sheng ◽  
Bin Wang ◽  
Aman Ullah ◽  
FaizaRiaz Khawaja
Keyword(s):  
Community Detection ◽  
Dynamic Networks ◽  
Parameter Tuning ◽  
Detection Algorithm ◽  
Network Evolution ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Information Dynamics ◽  
Dynamic Community Detection ◽  
Dynamic Community

Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.

A novel approach for choosing dynamic community detection algorithms using PROMETHEE II

2020 ◽  
Vol 15 (4) ◽  
pp. 327-341
Author(s):  
Samia Mohand Arab ◽  
Noria Taghezout ◽  
Fatima Zohra Benkaddour
Keyword(s):  
Community Detection ◽  
Promethee Ii ◽  
Detection Algorithms ◽  
Novel Approach ◽  
Dynamic Community Detection ◽  
Dynamic Community

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 Novel Emerging Topic Identification and Evolution Discovery Method on Time-Evolving and Heterogeneous Online Social Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoyan Xu ◽  
Wei Lv ◽  
Beibei Zhang ◽  
Shuaipeng Zhou ◽  
Wei Wei ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Online Social Networks ◽  
Detection Method ◽  
Local Network ◽  
Evolution Law ◽  
Dynamic Community Detection ◽  
Heterogeneous Social Networks ◽  
Propagation Network ◽  
Dynamic Community

With the fast development of web 2.0, information generation and propagation among online users become deeply interweaved. How to effectively and immediately discover the new emerging topic and further how to uncover its evolution law are still wide open and urgently needed by both research and practical fields. This paper proposed a novel early emerging topic detection and its evolution law identification framework based on dynamic community detection method on time-evolving and scalable heterogeneous social networks. The framework is composed of three major steps. Firstly, a time-evolving and scalable complex network denoted as KeyGraph is built up by deeply analyzing the text features of all kinds of data crawled from heterogeneous online social network platforms; secondly, a novel dynamic community detection method is proposed by which the new emerging topic is detected on the modeled time-evolving and scalable KeyGraph network; thirdly, a unified directional topic propagation network modeled by a great number of short texts including microblogs and news titles is set up, and the topic evolution law of the previously detected early emerging topic is identified by fully utilizing local network variations and modularity optimization of the “time-evolving” and directional topic propagation network. Our method is proved to yield preferable results on both a huge amount of computer-generated test data and a great amount of real online network data crawled from mainstream heterogeneous social networks.

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