Fast Community Discovery and Its Evolution Tracking in Time-Evolving Social Networks

This paper proposed a framework and an algorithm for identifying communities in dynamic social networks. In order to handle the drawbacks of traditional approaches for social network analysis, we utilize the community similarities and infrequent change of community members combined with community structure optimization to develop a Group-based social community identification model to analyze the change of social interaction network with multiple time steps. According to this model ,we introduced a greed-cut algorithm and depthsearch-first approach and combine them to develop a new algorithm for dynamic social interaction network recognition (called ADSIN). In addition, we conduct experiments on the dataset of Southern Women, the experiment results validate the accuracy and effectiveness of ADSIN. Key words-social network; community recognition; time step; φ

A cascade information diffusion based label propagation algorithm for community detection in dynamic social networks

This paper proposes a framework and an algorithm for identifying communities in dynamic social networks. In this paper, we introduce a social influence network which describes interpersonal behavior as linear time invariant (LTI) systems in order to define a measurement of influence among members in social networks based on the H2 norm. This paper focused on social news sites, proposes an approach to analyze relationships between groups and provides an algorithm to identify communities of members on social news sites. This paper also demonstrates an example of a social news site Digg.

This paper addresses a new problem concerning the evolution of influence relationships between communities in dynamic social networks. A weighted temporal multigraph is employed to represent the dynamics of the social networks and analyze the influence relationships between communities over time. To ensure the interpretability of the knowledge discovered, evolution of the influence relationships is assessed by introducing the Granger causality. Through extensive experiments, we empirically demonstrate the suitability of our model for studying the evolution of influence between communities. Moreover, we empirically show how our model is able to accurately predict the influence of communities over time using random forest regression.

Temporal dynamics of social interaction networks as well as the analysis of communities are key aspects to gain a better understanding of the involved processes, important influence factors, their effects, and their structural implications. In this article, we analyze temporal dynamics of contacts and the evolution of communities in networks of face-to-face proximity. As our application context, we consider four scientific conferences. On a structural level, we focus on static and dynamic properties of the contact graphs. Also, we analyze the resulting community structure using state-of-the-art automatic community detection algorithms. Specifically, we analyze the evolution of contacts and communities over time to consider the stability of the respective communities. Furthermore, we assess different factors which have an influence on the quality of community prediction. Overall, we provide first important insights into the evolution of contacts and communities in face-to-face contact networks.

Social networks are structures that aim to represent the relationships among the actors of a society. In the multiagent model, these networks depict the dependencies among the agents. The dependencies reflect the relation between the goals of the agents and the agents who have the power to achieve them. Like any social structure, also a multiagent system can be regulated by a set of norms and institutional powers that form an institution. Differently than informal social networks, the institutional social structure has an inherent dynamic character which cannot be captured by the current dependence networks. The networks have to reflect the changes of dependencies among agents created by the exercise of institutional powers. The aim of this paper is to define more dynamic social networks. From a description of an agent system it is possible to build a static dependence network. In the same way we describe how to pass from an abstract representation of what is an institution with its institutional powers to a dynamic social dependence network.

The article discusses the broad development of social recruitment - a phenomenon which involves interaction with potential candidates through social networks. Different methods of interaction in social networks with the candidates for the vacant position has been considered. It was found that the function of HR-branding at the company is usually assigned to the Manager of Social Affairs and media. A participation of the HR Manager has been suggested. The importance of creating of special Internet community of the company has been demonstrated and the selection criteria of social networks has been shown. All social networks have their own features. It was concluded that over the time the company which hosts its information and queries in social networks, gaining experience in identifying its target audience, finds preferred candidates and finds out the information for placing in her own Internet community. However, very often, this issue is not addressed at all or meets a sharply negative attitude of the leadership of the company. Leaders still distrust the Internet community in general and social networks in particular, seeing them as an opportunity to «stumble» on random people who are able to harm its activities. The inability to organize the work in social networks deprives the company of a powerful and modern tool to attract qualified personnel.

Animals interact with each other in species-specific reproducible patterns. These patterns of organization are captured by social network analysis, and social interaction networks (SINs) have been described for a wide variety of species including fish, insects, birds, and mammals. The aim of this study is to understand the evolution of social organization in Drosophila Using a comparative ecological, phylogenetic, and behavioral approach, the different properties of SINs formed by 20 drosophilids were compared. We investigate whether drosophilid network structures arise from common ancestry, a response to the species' past climate, other social behaviors, or a combination of these factors. This study shows that differences in past climate predicted the species' current SIN properties. The drosophilid phylogeny offered no value to predicting species' differences in SINs through phylogenetic signal tests. This suggests that group-level social behaviors in drosophilid species are shaped by divergent climates. However, we find that the social distance at which flies interact correlated with the drosophilid phylogeny, indicating that behavioral elements of SINs have remained largely unchanged in their evolutionary history. We find a significant correlation of leg length to social distance, outlining the interdependence of anatomy and complex social structures. Although SINs display a complex evolutionary relationship across drosophilids, this study suggests that the ecology, and not common ancestry, contributes to diversity in social structure in Drosophila.

Communities in social networks are groups of individuals who are connected with specific goals. Discovering information on the structure, members and types of changes of communities have always been of great interest. Despite the extensive global researches conducted on these, discovery has not been confirmed yet and researchers try to find methods and improve estimated techniques by using Data Mining tools, Graph Mining tools and artificial intelligence techniques. This paper proposes a novel two-phase approach based on global and local information to detect communities in social network. It explores the global information in the first phase and then exploits the local information in the second phase to discover communities more accurately. It also proposes a novel algorithm which exploits the local information and mines deeply for the second phase. Experimental results show that the proposed method has better performance and achieves more accurate results compared with the previous ones.

In recent years, the evolution of online social networks has become an important research topic in online social network analysis. An important approach to this problem is to detect community evolution events so as to understand the evolution of the whole network. Considering the huge amount of data in large social networks, an efficient and scalable community evolution detection algorithm is necessary. In this paper, we focus on community evolution events detection in dynamic social networks. First, we divide the Facebook and DBLP data set into a series of snapshots and apply the Louvain algorithm to find the communities in each snapshot. Then, we propose a light weight evolution events detection algorithm to find the community evolution patterns between adjacent snapshots, which statistically show the evolution trend of the entire network. Simulation results show that our algorithm can effectively detect the community evolution events in online social networks.

This article investigates the Multiple Equilibria Regulation (MER) model, i.e., an agent-based simulation model, to represent opinion dynamics in social networks. It relies on a small set of micro-prerequisites (intra-individual balance and confidence bound), leading to emergence of (non)stationary macro-outcomes. These outcomes may refer to consensus, polarization or fragmentation of opinions about taxation (e.g., congestion pricing) or other policy measures, according to the way communication is structured. In contrast with other models of opinion dynamics, it allows for the impact of both the regulation of intra-personal discrepancy and the interpersonal variability of opinions on social learning and network dynamics. Several simulation experiments are presented to demonstrate, through the MER model, the role of different network structures (complete, star, cellular automata, small-world and random graphs) on opinion formation dynamics and the overall evolution of the system. The findings can help to identify specific topological characteristics, such as density, number of neighbourhoods and critical nodes-agents, that affect the stability and system dynamics. This knowledge can be used to better organize the information diffusion and learning in the community, enhance the predictability of outcomes and manage possible conflicts. It is shown that a small-world organization, which depicts more realistic aspects of real-life and virtual social systems, provides increased predictability and stability towards a less fragmented and more manageable grouping of opinions, compared to random networks. Such macro-level organizations may be enhanced with use of web-based technologies to increase the density of communication and public acceptability of policy measures.

ABSTRACTWith online social networking having revolutionized the way in which individuals communicate and interact with each other, there is heightened research interest in the dynamics of social networks. This article seeks to contribute to this stream of research by addressing the key question of the impact of major life events, such as getting married or graduating from college, on social network evolution. Consistent with prior studies on the evolution of individuals’ social networks, we specifically focus on two key attributes of an individual's network: indegree of ties and relational embeddedness. By longitudinally analyzing the network activities of a large‐scale online social network, we find that the indegree of ties increased significantly following a major life event, and that this impact was stronger for more active users in the network. Interestingly, we also find that the broadcast of major life events served to revive dormant ties as reflected by a decrease in embeddedness following a life event. We also found that one‐time life events such as weddings had a greater impact than recurring life events such as birthdays on the evolution of a user's social network. From a research perspective, our study contributes to existing research by focusing on a user's communication network as opposed to friendship network and by emphasizing how exogenous life events add a different dimension to user communication patterns. The importance of life events on social network evolution has important implications for practice as well by providing insights to advertisers as well as to social networking sites.

A framework for exploring organizational structure in dynamic social networks

Online social networks (forum) have become extremely popular; numerous sites allow users to interact and share content using social links observing that there are some limitations on most of the social interaction networks. A recent study has demonstrated that the “strength of ties” varies widely, ranging from pairs of users who are best friends to pairs of users who even wished they weren’t friends. In order to distinguish between these strong and weak links, researchers have suggested examining the activity networks, the network that is formed by users who actually interact using one or many of the methods provided by the social networking site. Also for effective study, we collect data on both friendship relationship and interactions for a large subset of the Facebook social network and forum, Twitter, 2go, m.eskimi and other social interactive networks. From a technological standpoint, forums or boards are web applications managing user-generated content. Early Internet forums could be described as a web version of an electronic mailing list or newsgroup (such as exist on Usenet); allowing people to post messages and comment on other messages. It has been discovered that Chat forum is one of the newest way in which organization and the business world use now to reach out to their numerous customers in order to get feedback and responses concerning its entire product lines to share of their opinion.

Objective: The objective of this study was to identify the types of interactions between asthma patients and their social networks such as close family and friends that influence the management of asthma. Methods: Participants were Latino adults presenting for a repeat visit to the emergency department for asthma treatment. Qualitative interviews were conducted with 76 participants. They were asked to describe the experiences of their social networks that have asthma and how interactions with these individuals influenced their own asthma management. Responses were transcribed and analyzed using Grounded Theory as a qualitative analytic approach. Responses were assigned codes; similar codes were grouped into concepts and then categorized to form overarching themes. Results: Four themes emerged: (1) Perceptions of severity of asthma may be based on the experiences of social networks; (2) Economic factors may contribute to the sharing and borrowing of asthma medications between patients and their social networks; (3) Economic factors may contribute to using home remedies instead of prescribed medications; (4) Social network members may be unaware of the factors that trigger asthma and therefore, contribute to asthma exacerbations. Conclusion: This study identified important social network interactions that may impact asthma management in Latino adults. These results can be used to broaden the current focus of asthma self-management programs to incorporate discussions on the role of social networks. A focus on social network interactions addresses the social epidemiology of asthma and advances our understanding of root causes that may underlie the high prevalence of asthma in many Latino communities.