Social Networking

Social networks are a growing phenomenon in today’s Internet media consumption. Social networks are used to not only stay in touch with friends and family, but also to seek and receive information on specific products/services as well as social activism. Understanding and quantifying the information flow within these networks is, therefore, of great interest to individuals, groups and businesses. Several models have been proposed to describe the mechanism of spread of information. We describe these models in detail in this chapter. We then study the importance of “influencers” (nodes that have a higher influence on information spread in a network) and discuss the spread of both truthful and mis-information in a network. Methods to control the spread of mis-information through a social network is also discussed. We then discuss the inverse problem of discovering the source of any given piece of information. Both single and multi-source problems are considered.

Social networks are complex systems which evolve through interactions among a growing set of actors or users. A popular methodology of studying such systems is to use tools of complex network theory to analyze the evolution of the networks, and the topological properties that emerge through the process of evolution. With the exponential rise in popularity of Online Social Networks (OSNs) in recent years, there have been a number of studies which measure the topological properties of such networks. Several network evolution models have also been proposed to explain the emergence of these properties, such as those based on preferential attachment, heterogeneity of nodes, and triadic closure. We survey some of these studies in this chapter. We also describe in detail a preferential attachment based model to analyze the evolution of OSNs in the presence of restrictions on node-degree that are presently being imposed in all popular OSNs.

The importance of machine learning for social network analysis is realized as an inevitable tool in forthcoming years. This is due to the unprecedented growth of social-related data, boosted by the proliferation of social media websites and the embedded heterogeneity and complexity. Alongside the machine learning derives much effort from psychologists to build computational model for solving tasks like recognition, prediction, planning and analysis even in uncertain situations. In this chapter, we have presented different network analysis concepts. Then we have discussed implication of machine learning for network data preparation and different learning techniques for descriptive and predictive analysis. Finally we have presented some machine learning based findings in the area of community detection, prediction, spatial-temporal and fuzzy analysis.

Social networks of various kinds demonstrate a strong community effect. Actors in a network tend to form closely-knit groups; those groups are also called communities or clusters. Detecting such groups in a social network (i.e., community detection) remains a core problem in social network analysis. Among the challenges that face the researchers to come up with advanced community detection methods, there is a key challenge, which is the validation and evaluation of their methods. The limited benchmark data available, the lack of ground truth for many of the available network datasets, and the nature of the social behavior factor in the problem, turned the evaluation process to be very hard. Accordingly, understanding such challenges may help in designing good community detection methods. This chapter presents testing strategies for community detection approaches and explores a number of datasets that could be used in the testing process as well as stating some characteristics of those datasets.

Social networks have become popular and useful tools now a day. In the beginning researchers in social networks concentrated in studying static versions. However, the inclusion of time variations made the social networks dynamic. In the present day scenario the social networks are more dynamic than static. The introduction of societal networks by J. Fiksel is an evolutionary step in the study of social networks which originated the concept of dynamic social networks. The term societal network was used by Fiksel in order to distinguish it from the diversified ways in which the term ‘social network’ was used prior to the 1980s. There seems to be little work done following his approach. In this chapter we introduce the social structures which form the basis of Fiksel’s concept and also present his results. Some further study was made by Acharya using the graph theoretic concepts and also he proposed some directions of research. Dynamic social networks are studied recently from different angles. We also present some of these works and propose possible direction of research in this interesting field of research in social networks.

Social relationships and networking are key components of human life. Social network analysis provides both a visual and a mathematical analysis of human relationships. Recently, online social networks have gained significant popularity. This popularity provides an opportunity to study the characteristics of online social network graphs at large scale. An online social network graph consists of people as nodes who interact in some way such as members of online communities sharing information using relationships among them. In this paper a state of the art survey of the works done on community tracking in social network. The main goal is to provide a road map for researchers working on different measures for tracking communities in Social Network.

In Jharkhand, without having any effective measure of land reforms and the Panchayats as well as the absence of peasant mobilization, Total Literacy Campaign or organized women’s movement, major portion of the people having dependence upon the market forces have, no doubt, extended their livelihoods to various distant urban-industrial job markets. Unlike in Jharkhand, in West Bengal, economic and political / organizational changes have been taken place. Redistribution of land through land reforms, increase of wage rate and rise of Gram Panchayat have been as a source for the rural poor in Bengal. As a result, there is competition among the land owners to retain labourers and the land-owners’ authority has been weakened. Under the circumstances, in the present article, an attempt has been made to study the pattern of social networks of people in the two regions concerning social stratification by caste/ community composition, occupational class, ownership of land and their inter-face among themselves. For the study, four villages from Jharkhand and two from West Bengal were purposively selected and applying complete enumeration method and considering Head of a household or his representative as respondent, data on structural variables as well as few composite variables were collected. Besides household survey, methods of case history and group discussion with the people were also undertaken for collecting other information useful for the study. Then social network analysis techniques were adopted to analyze the data. Social Network Analysis has brought out that in Jharkhand the domain of articulation of ties of social help and support among the villagers against vulnerabilities, at the time of urgency or crisis, is primarily based upon traditional primordial relationship, kinship and, that way, spreads among the members of the same caste or community. In the Rarh region of West Bengal, its base is, on the other hand, secular. It circulates primarily along the ties of neighborhood and friendship. Recently, new secular relationships have been added to this list. But these are sort of contractual, exchange-oriented relationships as these are related to the employers, leading persons in different institutions or organizations, “experts”, influential persons and so on. The findings from the study may be much useful to the planners of development and methodologically, a triangulation of methods of case history, groups discussion with the concerned people and household survey may be effective strategy for future research study in the allied area.

Interaction across social networking sites leads to different kinds of ideas, concepts and choices and sharing or some nourishing effects, which might influence others to believe or trust. Seldom may this cause some malicious effect for members and their peers only. As social network is the domain for sharing opinion and comments, subsequently it can also propagate malicious signature as well. Security and privacy is essential component to protect user profile from this kind of malicious program, which basically evolves from any close acquaintances, that also belongs to same vector plane. The degree of malicious attack of a social network depends on the number of flow links from one user to another with forward operations. It is true that the probability of malicious attack evolves from friend’s community is of greater attack prone magnitude than the degree of attack from unknown members. This paper focuses the different verticals of such possibilities of attack under social network processes and also tries to investigate the rudimentary precautionary measure pertaining security algorithm behind it.

This chapter aims to bring to the reader an overview of the work done since the 9/11 terrorist attack, in the field of Social Network Analysis as a tool for understanding the underlying pattern /dynamics of terrorism and terrorist networks. SNA is particularly suitable for analyzing terrorist networks as it takes relationships into account rather than merely attributes, which are difficult to obtain for covert networks. Using graph theoretic methods and measures and open source data it has been possible to map terrorist networks and examine roles of different actors, as well as identify groups and structures within the network. The methodology is illustrated by reviewing two case studies: the 9/11 terrorist network study by Krebs, that used data from a single terrorist attack, and a study by Basu that used data from about 200 terrorist incidents in India to create a network of terrorist organizations for predictive purposes.

As the Internet continues to grow, the proliferation of online social networks raises many privacy concerns. The users of these OSNs are divulging endless details about their lives online. This personal information can be used by attackers to perpetrate significant privacy breaches and carry out attacks such as identity theft and credit card fraud. The privacy concerns arise from not just the users posting their personal information online, but also from OSNs publishing this information for analysis. Driven by Web 2.0 applications, more and more social network has been made publicly available. Preserving the privacy of individuals in this published data is an important concern. Although privacy preservation in data publishing has been studied extensively and several important models such as k- anonymity and l-diversity as well as many efficient algorithms have been proposed, most of the existing studies deal with relational data only. Those methods cannot be applied to social network data straightforwardly. Anonymization of social network data is a much more challenging task than anonymizing relational data. Firstly, in relational databases, attacks come from identifying individuals from quasi-identifiers. But in social networks, information such as neighbourhood graphs can be used to identify individuals. Secondly, tuples can be anonymized in relational data without affecting other tuples. But in social networks, adding edges or vertices affects the neighbourhoods of other vertices in the graph as well. In this chapter, we give a brief overview of the privacy concerns in online social networks and provide a detailed description of our algorithm, GASNA, a greedy algorithm for social network anonymization. This algorithm provides structural anonymity and sensitive attribute protection by achieving k-anonymity and l-diversity in social network data. We also discuss the challenges faced by the existing algorithms/models for social network data privacy and suggest techniques to counter these challenges. The issues discussed are the high cost of achieving k-anonymity when the value of k is fixed and the need for a better anonymity model which suits the current scenario of social networks. We also propose a new model called partial anonymity which can help reduce the number of edges added for anonymization when the value d of d-neighbourhood is greater than 1.

Social Network Analysis is a non-conventional Data Mining technique which analyzes social networks on web. The technique is used frequently for studying network behaviors using centrality measures viz. Degree, Betweenness, Closeness and Eigenvector. Hence has also led to the concept of Terrorist Network Mining which aims at detection of the terrorist group, studying the hierarchy they follow for the communication (using SNA) and then finally destabilizing of the network activities. The chapter focuses on an approach under SNA known as Brokerage which finds brokers who serve as the leading nodes in the network. Brokerage is expected to be beneficial in case of estimating the terrorist groups where different subgroups of terrorist organization coordinate to fulfill their awful deeds. The brokerage on whole estimates the influential roles as it would be done by individually calculating the centrality measures, with much more useful information aiding to amend terrorist network analysis.