Handbook of Social Network Technologies and Applications

Social network analysis (SNA), in essence, is not a formal theory in social science, but rather an approach for investigating social structures, which is why SNA is often referred to as structural analysis [1]. The most important difference between social network analysis and the traditional or classic social research approach is that the contexts of the social actor, or the relationships between actors are the first considerations of the former, while the latter focuses on individual properties. A social network is a group of collaborating, and/or competing individuals or entities that are related to each other. It may be presented as a graph, or a multi-graph; each participant in the collaboration or competition is called an actor and depicted as a node in the graph theory. Valued relations between actors are depicted as links, or ties, either directed or undirected, between the corresponding nodes. Actors can be persons, organizations, or groups – any set of related entities. As such, SNA may be used on different levels, ranging from individuals, web pages, families, small groups, to large organizations, parties, and even to nations.

Up to now, data of actual communication services obtained from communication networks, such as the volume of traffic and the number of users, has mainly been used to forecast traffic demands and provision network facilities. It can be said that this use focuses on the “quantitative” side of the data. On the other hand, such data can also illuminate several characteristics of the structures of the human society. This chapter introduces a new “qualitative” use of communication network data. We try to extract social information from the data, and investigate the universal structure of social networks that underlie the most popular communication services. Our expectation is that each communication service provides a different window on the universal social network structure. The question is how to access those windows.

The Social Web fosters novel applications targeting a more efficient and satisfying user guidance in modern social networks, e.g., for identifying thematically focused communities, or finding users with similar interests. Large scale and high diversity of users in social networks poses the challenging question of appropriate relevance/authority ranking, for producing fine-grained and rich descriptions of available partners, e.g., to guide the user along most promising groups of interest. Existing methods for graph-based authority ranking lack support for fine-grained latent coherence between user relations and content (i.e., support for edge semantics in graph-based social network models). We present TweetRank, a novel approach for

faceted

authority ranking in the context of social networks. TweetRank captures the additional latent semantics of social networks by means of statistical methods in order to produce richer descriptions of user relations. We model the social network by a 3-dimensional tensor that enables the seamless representation of arbitrary semantic relations. For the analysis of that model, we apply the PARAFAC decomposition, which can be seen as a multi-modal counterpart to common Web authority ranking with HITS. The result are groupings of users and terms, characterized by authority and navigational (hub) scores with respect to the identified latent topics. Sample experiments with life data of the Twitter community demonstrate the ability of TweetRank to produce richer and more comprehensive contact recommendations than other existing methods for social authority ranking.

This chapter deals with the analysis of interpersonal communication dynamics in online social networks and social media. Communication is central to the evolution of social systems. Today, the different online social sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements (i.e., image, video) as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information or concepts, and how the media channels impact our online interactional behavior. Our central hypothesis is that such communication dynamics between individuals manifest themselves via two key aspects: the information or

concept

that is the content of communication, and the

channel

i.e., the media via which communication takes place. We present computational models and discuss large-scale quantitative observational studies for both these organizing ideas. First, we develop a computational framework to determine the “interestingness” property of conversations cented around rich media. Second, we present user models of diffusion of social actions and study the impact of homophily on the diffusion process. The outcome of this research is twofold. First, extensive empirical studies on datasets from YouTube have indicated that on rich media sites, the conversations that are deemed “interesting” appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Second, observational and computational studies on large social media datasets such as Twitter have indicated that diffusion of social actions in a network can be indicative of future information cascades. Besides, given a topic, these cascades are often a function of attribute homophily existent among the participants. We believe that this chapter can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.

Social-networking sites have become an integral part of many users’ daily internet routine. Commercial enterprises have been quick to recognize this and are subsequently creating profiles for many of their products and services. Commercial enterprises use social network profiles to target and interact with potential customers as well as to provide a gateway for users of the product or service to interact with each other. Many commercial enterprises use the statistics from their product or service’s social network profile to tout the popularity and success of the product or service being showcased. They will use statistics such as number of friends, number of daily visits, number of interactions, and other similar measurements to quantify their claims. These statistics are often not a clear indication of the true popularity and success of the product. In this chapter the term product is used to refer to any tangible or intangible product, service, celebrity, personality, film, book, or other entity produced by a commercial enterprise.

Each chapter should be preceded by an abstract (10–15 lines long) that summarizes the content. The abstract will appear

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This chapter is a conceptual look at data quality issues that arise during scientific observations and their impact on social network analysis. We provide examples of the many types of incompleteness, bias and uncertainty that impact the quality of social network data. Our approach is to leverage the insights and experience of observational behavioral scientists familiar with the challenges of making inference when data are not complete, and suggest avenues for extending these to relational data questions. The focus of our discussion is on network data collection using observational methods because they contain high dimensionality, incomplete data, varying degrees of observational certainty, and potential observer bias. However, the problems and recommendations identified here exist in many other domains, including online social networks, cell phone networks, covert networks, and disease transmission networks.

A social network is a social structure between actors (individuals, organization or other social entities) and indicates the ways in which they are connected through various social relationships like friendships, kinships, professional, academic etc. Usually, a social network represents a social community, like a club and its members or a city and its citizens etc. or a research group communicating over Internet. In seventies Leinhardt [1] first proposed the idea of representing a social community by a digraph. Later, this idea became popular among other research workers like, network designers, web-service application developers and e-learning modelers. It gave rise to a rapid proliferation of research work in the area of social network analysis. Some of the notable structural properties of a social network are connectedness between actors, reachability between a source and a target actor, reciprocity or pair-wise connection between actors with bi-directional links, centrality of actors or the important actors having high degree or more connections and finally the division of actors into sub-structures or cliques or strongly-connected components. The cycles present in a social network may even be nested [2, 3]. The formal definition of these structural properties will be provided in Sect. 8.2.1. The division of actors into cliques or sub-groups can be a very important factor for understanding a social structure, particularly the degree of cohesiveness in a community. The number, size, and connections among the sub-groups in a network are useful in understanding how the network, as a whole, is likely to behave.

In the past, churn has been identified as an issue across most industry sectors. In its most general sense it refers to the rate of loss of customers from a company’s customer base. There is a simple reason for the attention churn attracts: churning customers mean a loss of revenue. Emerging from business spaces like telecommunications (telcom) and broadcast providers, where churn is a major issue, it is also regarded as a crucial problem in many other businesses, such as online games creators, but also online social networks and discussion sites. Companies aim at identifying the risk of churn in its early stages, as it is usually much cheaper to retain a customer than to try to win him or her back. If this risk can be accurately predicted, marketing departments can target customers efficiently with tailored incentives to prevent them from leaving.

It has been important for telecommunication companies to discover social networks from mobile subscribers. They have attempted to provide a number of recommendation services, but they realized that the services were not successful. In this chapter, we present semantic technologies for discovering social networks. The process is mainly composed of two steps; (1) profile identification and (2) context understanding. Through developing a Next generation Contents dElivery (NICE) platform, we were able to generate various services based on the discovered social networks.

Online identities play a critical role in the social web that is taking shape on the Internet. Despite many technical proposals for creating and managing online identities, none has received widespread acceptance. Design and implementation of online identities that are socially acceptable on the Internet remains an open problem. This chapter discusses the interplay between online identities and social networking. Online social networks (OSNs) are growing at a rapid pace and has millions of members in them. While the recent trend is to create explicit OSNs such as Facebook and MySpace, we also have implicit OSNs such as interaction graphs created by email and instant messaging services. Explicit OSNs allow users to create profiles and use them to project their identities on the web. There are many interesting identity related issues in the context of social networking including how OSNs help and hinder the definition of online identities.

There are many practical examples of social networks such as friendship networks or co-authorship networks. Detecting dense subnetworks from such networks are important for finding similar people and understanding the structure of factions. This chapter explains the definitions of communities, criteria for evaluating detected communities, methods for community detection, and actual tools for community detection.

Social media are not limited to text but also multimedia. Dailymotion, YouTube, and MySpace are examples of successful sites which allow users to share videos and interact among themselves. Due to the huge amount of videos, categorizing videos with similar contents can help users to search videos more efficiently. Unlike the traditional approach to group videos into some predefined categories, we propose to facilitate video searching with clustering from comment-based matrix factorization and to improve indexing via the generation of new concept words. Factorized component entropies are introduced for handling the difficult problem of vocabulary construction for concept discovery in social media. Since the categorization is learnt from users feedback, it can accurately represent the user sentiment on the videos. Experiments conducted by using empirical data collected from YouTube shows the effectiveness of our proposed methodologies.

In this chapter, we treat with the problem of selecting representative photographs corresponding to a given keyword for regions in the worldwide dimensions. Selecting and generating such representative photographs for representative regions from large-scale collections would help us understand about local specific objects and scenes with a worldwide perspective. We propose a solution to this problem using a large-scale collection of geotagged photographs. Our method firstly extracts the most relevant images by clustering and evaluation on the visual features. Then, based on geographic information of the images, representative regions are automatically detected. Finally, we select and generate a set of representative images for the representative regions by employing the Probabilistic Latent Semantic Analysis (PLSA) modelling. The results show the ability of our approach to mine regional representative photographs, and helps us understand how objects, scenes or events corresponding to the same given keywords are visually different and discover cultural differences depending on local regions over the world.

We present here a new technique for making predictions on recommender systems based on collaborative filtering. The underlying idea is based on selecting a different number of neighbors for each user, instead of, as it is usually made, selecting always a constant number

k

of neighbors. In this way, we have improved significantly the accuracy of the recommender systems.

Each chapter should be preceded by an abstract (10–15 lines long) that summarizes the content. The abstract will appear

online

at

www.SpringerLink.com

and be available with unrestricted access. This allows unregistered users to read the abstract as a teaser for the complete chapter. As a general rule the abstracts will not appear in the printed version of your book unless it is the style of your particular book or that of the series to which your book belongs. Please use the ’starred’ version of the new Springer

abstract

command for typesetting the text of the online abstracts (cf. source file of this chapter template

abstract

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Current Online social networks (OSN) are web services run on logically centralized infrastructure. Large OSN sites use content distribution networks and thus distribute some of the load by caching for performance reasons, nevertheless there is a central repository for user and application data. This centralized nature of OSNs has several drawbacks including scalability, privacy, dependence on a provider, need for being online for every transaction, and a lack of locality. There have thus been several efforts toward decentralizing OSNs while retaining the functionalities offered by centralized OSNs. A decentralized online social network (DOSN) is a distributed system for social networking with no or limited dependency on any dedicated central infrastructure. In this chapter we explore the various motivations of a decentralized approach to online social networking, discuss several concrete proposals and types of DOSN as well as challenges and opportunities associated with decentralization.

The emergence of the mediated social web – a distributed network of participants creating rich media content and engaging in interactive conversations through Internet-based communication technologies – has contributed to the evolution of powerful social, economic and cultural change. Online social network sites and blogs, such as Facebook, Twitter, Flickr and LiveJournal, thrive due to their fundamental sense of “community”. The growth of online communities offers both opportunities and challenges for researchers and practitioners. Participation in online communities has been observed to influence people’s behavior in diverse ways ranging from financial decision-making to political choices, suggesting the rich potential for diverse applications. However, although studies on the social web have been extensive, discovering communities from online social media remains challenging, due to the interdisciplinary nature of this subject. In this article, we present our recent work on characterization of communities in online social media using computational approaches grounded on the observations from social science.

There is no doubt that social websites have become one of the greatest inventions of the twenty-first century. Maintaining social connections, getting new and new friends, online entertainment: these are the very things we expect a good portal to provide. The concept of the social websites is that upon registration users share a desired amount of personal data with other users and after that they build a so called friend network using their acquaintances as building elements. The more acquaintances are present the more information is accessible during a certain period of time.

Over the last years, with the rapid advance in technology, it is becoming increasingly feasible for people to take advantage of the devices and services in the surrounding environment to remain “connected” and continuously enjoy the activity they are engaged in, be it sports, entertainment, or work. Such a ubiquitous computing environment will allow everyone permanent access to the Internet anytime, anywhere and anyhow [1]. Nevertheless, despite the evolution of services, social aspects remain in the roots of every human behavior and activities. Great examples of such phenomena are online social networks, which engage users in a way never seen before in the online world. At the same time, being aware and communicating context is a key part of human interaction and is a particularly powerful concept when applied to a community of users where services can be made more personalized and useful. Altogether, harvesting context to reason and learn about user behavior will further enhance the future multimedia vision where services can be composed and customized according to user context. Moreover, it will help us to understand users in a better way.

The rapidly growing global interconnectivity, brought about to a large extent by the Internet, has dramatically increased the importance and diversity of social networks. Modern social networks cut across a spectrum from benign recreational focused websites such as Facebook to occupationally oriented websites such as LinkedIn to criminally focused groups such as drug cartels to devastation and terror focused groups such as Al-Qaeda. Many organizations are interested in analyzing and extracting information related to these social networks. Among these are governmental police and security agencies as well marketing and sales organizations. To aid these organizations there is a need for technologies to model social networks and intelligently extract information from these models. While established technologies exist for the modeling of relational networks [1–7] few technologies exist to extract information from these, compatible with human perception and understanding. Data bases is an example of a technology in which we have tools for representing our information as well as tools for querying and extracting the information contained. Our goal is in some sense analogous. We want to use the relational network model to represent information, in this case about relationships and interconnections, and then be able to query the social network using intelligent human-centered concepts. To extend our capabilities to interact with social relational networks we need to associate with these network human concepts and ideas. Since human beings predominantly use linguistic terms in which to reason and understand we need to build bridges between human conceptualization and the formal mathematical representation of the social network. Consider for example a concept such as “leader”. An analyst may be able to express, in linguistic terms, using a network relevant vocabulary, properties of a leader. Our task is to translate this linguistic description into a mathematical formalism that allows us to determine how true it is that a particular node is a leader. In this work we look at the use of fuzzy set methodologies [8–10] to provide a bridge between the human analyst and the formal model of the network.

In recent years, there is a dramatic growth in number and popularity of online social networks. There are many networks available with more than 100 million registered users such as Facebook, MySpace, QZone, Windows Live Spaces etc. People may connect, discover and share by using these online social networks. The exponential growth of online communities in the area of social networks attracts the attention of the researchers about the importance of managing trust in online environment. Users of the online social networks may share their experiences and opinions within the networks about an item which may be a product or service. The user faces the problem of evaluating trust in a service or service provider before making a choice. Recommendations may be received through a chain of friends network, so the problem for the user is to be able to evaluate various types of trust opinions and recommendations. This opinion or recommendation has a great influence to choose to use or enjoy the item by the other user of the community. Collaborative filtering system is the most popular method in recommender system. The task in collaborative filtering is to predict the utility of items to a particular user based on a database of user rates from a sample or population of other users. Because of the different taste of different people, they rate differently according to their subjective taste. If two people rate a set of items similarly, they share similar tastes. In the recommender system, this information is used to recommend items that one participant likes, to other persons in the same cluster. But the collaborative filtering system performs poor when there is insufficient previous common rating available between users; commonly known as cost start problem. To overcome the cold start problem and with the dramatic growth of online social networks, trust based approach to recommendation has emerged. This approach assumes a trust network among users and makes recommendations based on the ratings of the users that are directly or indirectly trusted by the target user.

Social Network Services

(SNS) are currently drastically revolutionizing the way people interact, thus becoming

de facto

a predominant service on the web, today.

1

The impact of this paradigm change on socioeconomic and technical aspects of collaboration and interaction is comparable to that caused by the deployment of World Wide Web in the 1990s.

In Social Network Analysis (SNA) it is quite conventional to employ graph theory concepts for example cut-points and cut-sets or measuring node centralities like betweenness, degree and closeness to highlight important actors in the network. However, it is also believed that most of these measures alone are inadequate for investigating terrorist/covert networks. In this paper we compute posterior probability using Bayes conditional probability theorem to compute each nodes positional probability to see how probable/likely that the node is a key player. Obviously, larger the probability value higher is the chances that the node is a key actor, the system after words using the computed probability can select an arbitrary number of nodes for elimination to make the network non-functional or severely destroying its capability. We know that that network fragmentation in its simplest form is a count of the number of components, more the number of counts larger is the fragmentation. The reason of calling it simple is because it does not include many aspects of the network for example structure etc. Borgatti has provided a similar measure which does includes the shape and internal structure of the components and high light the important actors in the network. Our computational procedure provides very similar results in highlighting the key player problem. We simulated our computational model through various random networks and we also applied it to the David Krackhardt’s kite network and to 9-11 hijackers network to bench mark our results. It shows that conceptually this framework of evaluation can be used to highlight the key players in terrorist/covert cells.

Internet users communicate with each other in various ways: by Emails, instant messaging, social networking, accessing Web sites, etc. In the course of communicating, users may unintentionally copy files contaminated with computer viruses and worms [1, 2] to their computers and spread them to other users [3]. (Hereafter we will use the term “threats”, rather than computer viruses and computer worms). The Internet is the chief source of these threats [4].

A social network is a structure of individuals or organizations, which are connected by one or more types of interdependency, such as friendship, affinity, common interests or knowledge. Social networks use now web 2.0 technology and the users may need to follow a series of restrictions or conditions to join or add contents. We look here at their context and threats, in order to ascertain their needs for security. We propose the use of patterns to specify these requirements in a precise way and we present two specific patterns. A pattern is an encapsulated solution to a software problem in a given context. We present here the Participation-Collaboration Pattern, which describes the functionality of the collaboration between users in applications and the Collaborative Tagging Pattern, which is useful to share content using keywords to tag bookmarks, photographs and other contents. We also discuss possible improvements to the current situation.

With the ubiquitous characteristic of the Internet, today many online social environments are provided to connect people. Various social relationships are thus created, connected, and migrated from our real lives to the Internet environment from different social groups. Many social communities and relationships are also quickly constructed and connected via instant personal messengers, blogs, Twitter, Facebook, and a great variety of online social services. Since social network visualizations can structure the complex relationships between different groups of individuals or organizations, they are helpful to analyze the social activities and relationships of actors, particularly over a large number of nodes. Therefore, many studies and visualization tools have been investigated to present social networks with graph representations. In this chapter, we will first review the background of social network analysis and visualization methods, and then introduce various novel visualization applications for social networks. Finally, the challenges and the future development of visualizing online social networks are discussed.

In the last decade, the popularity of social networking applications has dramatically increased. Social networks are collection of persons or organizations connected by relations. Members of Facebook listed as friends or persons connected by family ties in genealogical trees are examples of social networks. Today’s web surfers are often part of many online social networks: they communicate in groups or forums on topics of interests, exchange emails with their friends and colleagues, express their ideas on public blogs, share videos on YouTube, exchange and comment photos on Flickr, participate to the edition of the online encyclopedia Wikipedia or contribute to daily news by collaborating to Wikinews or Agoravox.

A social network [2] is a description of the social structure between actors, mostly persons, groups or organizations. It indicates the ways in which they are connected with each other by some relationship such as friendship, kinship, finance exchange etc. In a nutshell, when the person uses already known/unknown people to create new contacts, it forms social networking. The social network is not a new concept rather it can be formed when similar people interact with each other directly or indirectly to perform particular task. Examples of social networks include a friendship networks, collaboration networks, co-authorship networks, and co-employees networks which depict the direct interaction among the people. There are also other forms of social networks, such as entertainment networks, business Networks, citation networks, and hyperlink networks, in which interaction among the people is indirect. Generally, social networks operate on many levels, from families up to the level of nations and assists in improving interactive knowledge sharing, interoperability and collaboration.

Online social networks offer opportunities to analyze user behavior and social connectivity and leverage resulting insights for effective online advertising. This chapter focuses on the role of social network information in online display advertising.

Until recent developments in digital-based innovation, companies were defined by how they made use of resources which were tangible things such as equipment, land, raw materials and human talent for the purpose of supplying goods and services to the economy [37]. Such companies had a clearly defined central management structure which was responsible for the general policies under which the company’s hierarchy operated with well delineated reporting relationships and job responsibilities [47]. Within this rigid hierarchical organizational structure, decision making was bureaucratic and an anti-innovation bias was prevalent [55]. Even with the development of electronic communications and computing systems, innovation was relegated to the purview of professional R&D departments [22] within a highly structured corporate environment [51]. Indeed, in 1992, when managers were surveyed about the structure of their companies, most answered that their companies were still structured in a very traditional way, that is, with standardized jobs, procedures and policies and a hierarchical organization which emphasized a top-down chain of command [6].