Chapter 1 - Why Design to Thrive?

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.

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.

Users are members of communities on social networks. Users’ interested topics keep changing, resulting in the change of their communities’ interested topics as well. Level, period of time, and interested topics represent features of a community which (i) change upon preferences of each user on social networks for making friends or being interested in topics (based on message content); (ii) are formed or change from online groups of friends or the suggestions to make friends. Hence, the link of users in communities can be viewed as a network of users by their features in social network communities. In this paper, the author studies and proposes a new model for discovering communities using Temporal-Author-Recipient-Topic (TART) model combined with Kohonen neural networks to discover communities of users with the same interested topics over different periods of time. The research goal is achieved through testing models on two Vietnamese datasets (collected from social networks at universities and online newspapers).

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.

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 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 detection is an effective approach to unveil relationships among individuals in online social networks. In the literature, quite a few algorithms have been proposed to conduct community detection by exploiting the topology of social networks and the attributes of social actors. In practice, community detection is usually conducted by third parties, such as advertisement companies and hospitals, with access to social networks for different purposes, which can easily lead to a privacy breach. In this paper, we investigate community detection in social networks aiming to protect the privacy of both the network topology and the users' attributes. We show that with additional prior knowledge, community detection can be performed by querying the information of only a fraction of instead of the entire population. In particular, we first propose a new scheme called differentially private community detection (DPCD). DPCD detects communities in social networks via a probabilistic generative model, which can be decomposed into subproblems solved by individual users. The private social relationships and attributes of each user are protected by objective perturbation with differential privacy guarantees. Then, we propose a parsimonious node affiliation recovery (NAR) algorithm, which is also differentially private, to unveil the community affiliation information of the whole population based on that of the limited number of queried individuals by solving a sparse optimization problem. Through both theoretical analysis and experimental validation using synthetic and real-world social networks, we demonstrate that the proposed DPCD scheme detects social communities under the modest privacy budget. In addition, we show the effectiveness of NAR to perform community detection by querying a limited number of individuals in social 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.

Online communities used as resource enlargement in open innovation processes are a promising concept. Yet, to date few comparative studies on characteristics of different online communities have been done. This paper identifies the cultures of innovation communities and brand communities in the environment of the Web 2.0 and shows how to use and further exploit their potential in different steps of open innovation projects. To analyze these online communities, an exploratory case study design with ten small- and medium-sized enterprises (SMEs) was chosen. All ten enterprises worked with the same innovation intermediary, which implemented an innovation community platform into a social network and possess a brand community in the respective social network.

Artificial neural networks are a machine learning method ideal for solving classification and prediction problems using Big Data. Online social networks and virtual communities provide a plethora of data. Artificial neural networks have been used to determine the emotional meaning of virtual community posts, determine age and sex of users, classify types of messages, and make recommendations for additional content. This article reviews and examines the utilization of artificial neural networks in online social network and virtual community research. An artificial neural network to predict the maintenance of online social network “friends” is developed to demonstrate the applicability of artificial neural networks for virtual community research.

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.

Many famous online social networks, e.g., Facebook and Twitter, have achieved great success in the last several years. Users in these online social networks can establish various connections via both social links and shared attribute information. Discovering groups of users who are strongly connected internally is defined as the community detection problem. Community detection problem is very important for online social networks and has extensive applications in various social services. Meanwhile, besides these popular social networks, a large number of new social networks offering specific services also spring up in recent years. Community detection can be even more important for new networks as high quality community detection results enable new networks to provide better services, which can help attract more users effectively. In this paper, we will study the community detection problem for new networks, which is formally defined as the “New Network Community Detection” problem. New network community detection problem is very challenging to solve for the reason that information in new networks can be too sparse to calculate effective similarity scores among users, which is crucial in community detection. However, we notice that, nowadays, users usually join multiple social networks simultaneously and those who are involved in a new network may have been using other well-developed social networks for a long time. With full considerations of network difference issues, we propose to propagate useful information from other well-established networks to the new network with efficient information propagation models to overcome the shortage of information problem. An effective and efficient method, Cat (Cold stArT community detector), is proposed in this paper to detect communities for new networks using information from multiple heterogeneous social networks simultaneously. Extensive experiments conducted on real-world heterogeneous online social networks demonstrate that Cat can address the new network community detection problem effectively.

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.

: 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.