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.

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 Core-Based Dynamic Community Detection in Mobile Social Networks

Entropy ◽  
2013 ◽  
Vol 15 (12) ◽  
pp. 5419-5438 ◽  
Author(s):  
Hao Xu ◽  
Yanli Hu ◽  
Zhenwen Wang ◽  
Jianwei Ma ◽  
Weidong Xiao
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Mobile Social Networks ◽  
Dynamic Community Detection ◽  
Dynamic Community

scholarly journals Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jun Ge ◽  
Lei-lei Shi ◽  
Lu Liu ◽  
Hongwei Shi ◽  
John Panneerselvam
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Network Structure ◽  
Online Social Networks ◽  
Link Prediction ◽  
User Behavior ◽  
Detection Algorithm ◽  
Factors Affecting ◽  
Friend Recommendation ◽  
Dynamic Community Detection

Link prediction in online social networks intends to predict users who are yet to establish their network of friends, with the motivation of offering friend recommendation based on the current network structure and the attributes of nodes. However, many existing link prediction methods do not consider important information such as community characteristics, text information, and growth mechanism. In this paper, we propose an intelligent data management mechanism based on relationship strength according to the characteristics of social networks for achieving a reliable prediction in online social networks. Secondly, by considering the network structure attributes and interest preference of users as important factors affecting the link prediction process in online social networks, we propose further improvements in the prediction process by designing a friend recommendation model with a novel incorporation of the relationship information and interest preference characteristics of users into the community detection algorithm. Finally, extensive experiments conducted on a Twitter dataset demonstrate the effectiveness of our proposed models in both dynamic community detection and link prediction.

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