МИГРАНТЫ В СОЦИАЛЬНЫХ СЕТЯХ: УХОД ОТ РЕАЛЬНОСТИ ИЛИ ПОРОЖДЕНИЕ НОВОЙ ЦИФРОВОЙ РЕАЛЬНОСТИ

The article is devoted to the topical issue of identifying information threats in virtual communities of social networks and mechanisms for counteracting such threats. The process of organization of monitoring and content analysis of virtual communities in social networks is studied. The technology of detecting information threats of virtual communities in social networks and methods of their assessment have been developed. An approach to finding pages of discussions on social networks based on the Louvain method is proposed. The technology of determining the degree of information threat of the virtual community in social networks and ways to counteract the information threats of the virtual community have been developed.

Communities in social networks form with different purposes and play a significant role in interpersonal interactions. Analysis of virtual communities indicates a more precise understanding of the behaviours and desires of individuals in social networks. In this paper, new measures have been proposed for analyzing implicit and explicit communities in Online Social Networks (OSNs). The measures of “potential value of the community members” and “value of the community messages”, which are used for calculating the measure of “community value” are among the most important measures introduced in this paper. Another measure introduced is “user influence rate” in a community, which represents the contribution of a person in creating value in a community. To provide a sound dataset, we collected the information from several real implicit communities in Twitter based on different hashtags. Finally, the suggested measures have been analyzed and compared statistically and behaviourally across different communities. The results of this research well indicate the importance and practicality of the measures introduced in Community analysis of Twitter.

Chapter 1 - Why Design to Thrive?

The conventional notion of community that favors a high ratio of internal edges to outbound edges becomes invalid when each vertex participates in multiple communities. Such a behavior is commonplace in social networks. The significant overlaps among communities make most existing community detection algorithms ineffective. The lack of effective and efficient tools resulted in very few empirical studies on large-scale detection and analyses of overlapping community structure in real social networks. We developed recently a scalable and accurate method called the Partial Community Merger Algorithm (PCMA) with linear complexity and demonstrated its effectiveness by analyzing two online social networks, Sina Weibo and Friendster, with 79.4 and 65.6 million vertices, respectively. Here, we report in-depth analyses of the 2.9 million communities detected by PCMA to uncover their complex overlapping structure. Each community usually overlaps with a significant number of other communities and has far more outbound edges than internal edges. Yet, the communities remain well separated from each other. Most vertices in a community are multi-membership vertices, and they can be at the core or the peripheral. Almost half of the entire network can be accounted for by an extremely dense network of communities, with the communities being the vertices and the overlaps being the edges. The empirical findings ask for rethinking the notion of community, especially the boundary of a community. Realizing that it is how the edges are organized that matters, the f-core is suggested as a suitable concept for overlapping community in social networks. The results shed new light on the understanding of overlapping community.

This chapter explores the topic of virtual community discovery in social networks. It starts with defining virtual communities. After that, the chapter goes over several network modelling strategies and community identification techniques. The significance of standardised datasets in evaluating algorithm performance is shown by a thorough analysis of benchmark networks used to test community detection techniques. The accuracy indices for assessing virtual community identification algorithms are also covered. The chapter offers common datasets needed to evaluate virtual community discovery techniques. Next, it explores community discovery using the Girvan-Newman and Louvain algorithms, two well-known techniques, to demonstrate practical applications. These illustrations explain how theoretical ideas can be applied in practical settings. Finally, this chapter provides a thorough overview of the concepts and assessment criteria for virtual community discovery in social networks, providing insightful information for both scholars and industry professionals.

The research on communities in social networks takes many paths in the literature, among which: the problematic of accurately detecting communities; modeling the evolution of those communities within the evolving network; and then finding the patterns that characterize this evolution over time. In our work, we focused on the problematic of detecting communities in social networks based on the information disseminated among users of the social network and the type of content shared by these users. The work at hand consists of a brief introduction to the subject and the problem definition, then we move to state the main contribution of our work which consists of a multi-layer model to detect communities of users based on the content shared by users, the lowest layer would detect topics of interest of each user while the upper layer would form communities from generated topics. We conclude the paper stating our perspectives and future works.

: Current approaches to community detection in social networks often ignore the spatial location of the nodes. In this paper, we look to extract spatially-near communities in a social network. We introduce a new metric to measure the quality of a community partition in a geolocated social networks called spatially-near modularity a value that increases based on aspects of the network structure but decreases based on the distance between nodes in the communities. We then look to find an optimal partition with respect to this measure - which should be an ideal community with respect to both social ties and geographic location. Though an NP-hard problem, we introduce two heuristic algorithms that attempt to maximize this measure and outperform nongeographic community finding by an order of magnitude. Applications to counter-terrorism are also discussed.

Searching for a community based on query nodes in a graph is a fundamental problem and has been extensively investigated. Most of the existing approaches focus on finding a community in a social network, and very few studies consider location-based social networks where users can check in locations. In this paper we propose the GeoSocial Community Search problem (GCS) which aims to find a social community and a cluster of spatial locations that are densely connected in a location-based social network simultaneously. The GCS can be useful for marketing and user/location recommendation. To the best of our knowledge, this is the first work to find a social community and a cluster of spatial locations that are densely connected from location-based social networks. We prove that the problem is NP-hard, and is not in APX, unless P = NP. To solve this problem, we propose three algorithms: core-based basic algorithm, top-down greedy removing algorithm, and an expansion algorithm. Finally, we report extensive experimental studies that offer insights into the efficiency and effectiveness of the proposed solutions.

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.

Community identif ication has been a major research area in social network analysis. One popular type of community is one in which every member of the community knows every other member, which can be viewed as a clique in a graph representing the social network. In this paper, we present a novel highly scalable method for finding large maximal cliques that is validated with experimental results on several real-life social networks. In addition, while the importance of finding tightly knit communities has been widely accepted, the influence of community on the behavior of the individuals belonging to those communities is relatively unexplored. We also attempt to answer various questions in the context of cliques as communities in telecom social networks: how individuals in communities behave, what influence a community has on the behavior of an individual, and whether communities have a characteristic behavior of their own. We also examine whether the behavior of individuals who belong to communities differs from those who do not. We believe that the findings of such a study will reassert the importance of finding communities in telecom social networks and will help telecom operators improve group targeting and customer relationship management.

Extended-Reality Technologies: An Overview of Emerging Applications in Medical Education and Clinical Care.

During the last decade, the social networks have known a huge popularity due to their ease of connecting people. The community detection has been in the center of attention in the analysis of this kind of networks. However, this area is still a very active field of research; the majority of methods involving this problem suffers from the accuracy in the determination of meaningful modules or the computational complexity of the used algorithm. In this paper, our contribution is to propose a new method that reveals the communities in social networks based on an excellent similarity measure and the minimum spanning tree. Our approach guaranties both accuracy and efficiency of the resulted partitions when compared with the existing methods and considering five real world social networks with a very promising computational complexity.

The social networks are renowned for their dynamism in network formation among the existing and new arrivals of the online community. The proposed algorithm, Communalyzer, presents the life cycle of online community which involves the phases like birth, growth, shrinkage, merging, split, and death. The proposed community mining algorithm is a two phase process that performs the evolutionary operations and detects the overlapping community. The detection is looked upon as two type’s namely mining of local and global community. In the former community mining, the community structure can be viewed by iteratively traversing the neighboring nodes to determine the live node and the limits/boundary of the community. The global community mining describes the dynamism in community evolution and other operations. The proposed research work uses Wiki election dataset.

The rise of online communities in Internet environments has set in motion an unprecedented shift in power from vendors of goods and services to the customers who buy them, with those vendors who understand this transfer of power and choose to capitalize on it by organizing online communities and being richly rewarded with both peerless customer loyalty and impressive economic returns. A type of online community, the virtual world, could radically alter the way people work, learn, grow consume, and entertain. Understanding the exchange of social and economic capital in online communities could involve looking at what causes actors to spend their resources on improving someone else’s reputation. Actors’ reputations may affect others’ willingness to trade with them or give them gifts. Investigating online communities reveals a large number of different characters and associated avatars. When an actor looks at another’s avatar they will evaluate them and make decisions that are crucial to creating interaction between customers and vendors in virtual worlds based on the exchange of goods and services. This chapter utilizes the ecological cognition framework to understand transactions, characters and avatars in virtual worlds and investigates the exchange of capital in a bulletin board and virtual. The chapter finds strong evidence for the existence of characters and stereotypes based on the ecological cognition framework and empirical evidence that actors using avatars with antisocial connotations are more likely to have a lower return on investment and be rated less positively than those with more sophisticated appearing avatars.

Community detection is a substantial technique to find out the relationship between nodes in complex networks. By understanding the behavior of elements in a community, one can predict the overall feature of the large scale social network. Detecting different communities in large scale network is a challenging task due to huge data size associated with such network. The main purpose of this paper is finding distinct communities. For this reason, in this paper after using limited Random Walk to detect nodes feature set, nodes that share higher common feature set form a community. Experimental results in real and artificial networks show, with great accuracy, that the proposed method succeeds to recover communities in the network.