Implicit Virtual Communities in Social Networks

The objective of this study is to discover social communities in a social network using different social network community discovery methods that utilize metrics and structures like degree, clustering coefficient, k-cores, weak and strong components. We have used two different datasets and methods: K-core community discovery method for DBLP dataset and Main Path Analysis method for Arxiv High-energy physics theory citation network. At the end of the analyses, we have obtained several reports that represent the skeleton structure of the communities in the networks.

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.

The distribution of user community in social network has the problem of similar feature distribution, which leads to the high viscosity between community and community, and it is difficult to mine community features. A social network community discovery algorithm is proposed based on parallel recommendation. The fuzzy decision model of social network community discovery is established by taking the user behavior value, user consumption value and loyalty of social network as independent variables. The relationship between the interaction degree and the recommendation effect in the social network is analyzed by extracting the characteristic quantity of the community association attribute in the social network, and the community group recommendation in the social network is carried out by using the parallel recommendation algorithm, and the number of visits between users is analyzed. The number of messages is used as the weighted weight coefficient, the difference factor is introduced to evaluate the influence of the community, the synchronous label of the social network community is established, and the community feature mining and parallel recommendation are realized according to the label location. The simulation results show that, the proposed method has good accuracy in community discovery, high accuracy in community attribute feature mining, and low cost of the algorithm. It has obvious advantages compared with similar algorithms.

Chapter 1 - Why Design to Thrive?

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.

With the rapid development of various social platforms, social networks have accumulated more and more data. These huge and complex data contain valuable information in many fields. It is of far-reaching significance to conduct statistical analysis of these data and dig out valuable information and apply it in real life. Based on the homogeneity of social networks, our paper proposes a framework of discovery algorithms based on the homogeneity of microblogs to realize the theme community discovery of microblog social networks. Firstly, the data set is preprocessed, the invalid data is removed and classified by microblog users. Secondly, the LDA model is used to extract the topic of microblog user blogs to describe the characteristics of microblog users. Thirdly, two users are calculated based on the theme interest. The homogeneity measure between them is used to represent the relationship of social networks. Finally, the community of the implicit social network is realized through unsupervised algorithms to build communities with the same interest as the theme. In order to verify the validity of our proposed algorithm framework, this paper uses data sets from microblog network to conduct experiments. The experimental results show that the algorithm effectively combines the homogeneity measure of the network and has achieved very good results in the implicit community discovery.

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to find a way to use computers to discover social ties that form between community members just from the digital footprints left behind in their online forum postings automatically. As part of this work, a web-based system for content and network analysis called the Internet Community Text Analyzer (ICTA) is being developed. A prototype of ICTA is available at http://textanalytics.net.

The increasing amount of communication between individuals in e-formats (e.g. email, Instant messaging and the Web) has motivated computational research in social network analysis (SNA). Previous work in SNA has emphasized the social network (SN) topology measured by communication frequencies while ignoring the semantic information in SNs. In this paper, we propose two generative Bayesian models for semantic community discovery in SNs, combining probabilistic modeling with community detection in SNs. To simulate the generative models, an EnF-Gibbs sampling algorithm is proposed to address the efficiency and performance problems of traditional methods. Experimental studies on Enron email corpus show that our approach successfully detects the communities of individuals and in addition provides semantic topic descriptions of these communities.

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.

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.

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.

Discovery of communities in networks is a fundamental data analysis problem. Most of the existing approaches have focused on discovering communities of nodes, while recent studies have shown great advantages and utilities of the knowledge of communities of links. Stochastic models provides a promising class of techniques for the identification of modular structures, but most stochastic models mainly focus on the detection of node communities rather than link communities. We propose a stochastic model, which not only describes the structure of link communities, but also considers the heterogeneous distribution of community sizes, a property which is often ignored by other models. We then learn the model parameters using a method of maximum likelihood based on an expectation-maximization algorithm. To deal with large complex real networks, we extend the method by a strategy of iterative bipartition. The extended method is not only efficient, but is also able to determine the number of communities for a given network. We test our approach on both synthetic benchmarks and real-world networks including an application to a large biological network, and also compare it with two existing methods. The results demonstrate the superior performance of our approach over the competing methods for detecting link communities.

Nonnegative matrix factorization (NMF) has become a powerful model for community discovery in complex networks. Existing NMF-based methods for community discovery often factorize the corresponding adjacent matrix of complex networks to obtain its community indicator matrix. However, the adjacent matrix cannot represent the global structure feature of complex networks very well, and this leads to the performance degradation of community discovery. Besides, most of existing methods are not robust and scalable enough, so they are not effective to deal with complex networks with noises and large scales. Aiming at these problems above, in this paper we propose a method for community discovery using distributed robust NMF with SimRank similarity measure. This method selects SimRank measure to construct the feature matrix, which can more accurately represent the global structure feature of complex networks. To improve the robustness, we select $$\ell _{2,1}$$ norm instead of the widely used Frobenius norm to construct its NMF-based community discovery model. In addition, to improve the scalability, we implement its key components by using MapReduce distributed computing framework, including computing SimRank feature matrix and iteratively solving the NMF-based model for community discovery. We conduct extensive experiments on several typical complex networks. The results show that our method has better performance and robustness than other representative NMF-based methods for community discovery. Moreover, our method presents good scalability and hence can be used to discover communities in the large-scale complex networks.

Objective of the study. Migrants began to see in social networks a solution for many of their own issues, including information problems, forming a virtual identity – acquiring virtual values instead of real ones, organizing into virtual communities in general, into virtual ethnic digital diasporas. This formulates the main goal of this research in the form of studying the phenomenon of the digital diaspora and digital migrant as an object and subject of this research. The study of this new reality, its relation to the traditional reality is an important and urgent research task, since it gives rise to new tendencies and trends, the comprehension of which fills with new content such problems as the adaptation of migrants, their new, including digital identity, which characterizes in a new way social and ethnic capital of migrants. The methodological basis of the research. For a systematic understanding of the subject of this study, it is necessary to comprehend digital youth migration in the form of a withdrawal to digital reality in the form of an ethnic digital diaspora as a new type of informational sociality; it is also required to comprehend this phenomenon as migrant social media. This approach is implemented in the works of such Russian researchers as A.P. Glukhov, G.Z. Efimova, Z. V. Zyuban, S.O. Kremleva and others. It should be noted that understanding the concepts of “digital migrant” and “digital diaspora” also involves the use of the principles of reality explication (social, communication, consensus and media reality), understanding the construct as a virtual model for explaining reality. Research results. Young people became a generation of social networks, spending most of their lives there, organizing themselves into network communities according to their interests and needs, establishing communication virtual connections, preferring virtual reality to traditional reality, as a result,  they form a virtual identity, acquiring virtual values instead of real ones, spending most of their productive time in the world of virtual communication, computer games and aimless wandering around the sites. This creates the basis for replacing traditional reality with virtual reality and significantly transforms the social behavior of young people who immigrate their real world to the virtual world, turning this generation into online migrants.   Prospects of the study. It is necessary to study the behavioral patterns of youth network migrants, and this is connected with the study of the forms of network socialization, the specifics of communication in social networks, the peculiarities of the formation of virtual identity and, of course, the analysis of those informational youth values that oppose the virtual world to the real world.

Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. However, many large networks often lack a particular community organization at a global level. In these cases, traditional graph partitioning algorithms fail to let the latent knowledge embedded in modular structure emerge, because they impose a top-down global view of a network. We propose here a simple local-first approach to community discovery, able to unveil the modular organization of real complex networks. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, i.e. its ego neighborhood, using a label propagation algorithm; finally, the local communities are merged into a global collection. We tested this intuition against the state-of-the-art overlapping and non-overlapping community discovery methods, and found that our new method clearly outperforms the others in the quality of the obtained communities, evaluated by using the extracted communities to predict the metadata about the nodes of several real world networks. We also show how our method is deterministic, fully incremental, and has a limited time complexity, so that it can be used on web-scale real networks.