Social Network Analysis

With the rapid growth of Internet and social networking websites, there are various services that provided in these platforms. For instance, Facebook focuses on social activities, Twitter and Plurk are both focus on the interaction of users through short messages (which are so-called microblogs). Therefore, there are more than millions of users registered in these websites and become places where full of marketing possibilities. Thus, it is an important issue to assist companies to understand the users in the social networking websites in order to enhance the accuracy and efficiency of target marketing. In this paper, we have proposed the architecture of a social recommendation system based on the data from microblogs. The social recommendation system is conducted according to the messages and social structure of target users. The similarity of the discovered features of users and products will then be calculated as the essence of the recommendation engine. A case study will be included to present how the recommendation system works based on real data that collected from Plurk.

This paper reports a 5-year design experiment on cumulative knowledge building as part of an international project. Through a longitudinal study and analysis of cumulative research data, we sought to answer the question, 'what happened and why in knowledge building?' Research data constitute messages which participants have written into a shared knowledge building database. A multi-method approach combing quantitative and qualitative data was adopted which integrated analysis of message generation, content analysis, network analysis, structure of message threads, discourse analysis and interviews. Conclusions are based on analysis of almost 2000 messages. Qualitative content analysis reveals 14 main categories of data. When the content of the messages are analysed, quantitatively cumulative trends emerge. When the frequencies of messages are plotted against time, peaks and troughs of message writing are revealed. The explanations for these patterns and variations are sought through interviews. Social network analysis shows that the network is centralised. The research literature suggests that decentralised networks are ideal, but in this particular case, the expert centralisation was beneficial for knowledge building in the collaborative and associated professional networks. The reasons for this are discussed.

Previous qualitative studies show that when the formal organization of a school and patterns of informal interaction are aligned, faculty and leaders in a school are better able to coordinate instructional change. This article combines social network analysis with interview data to analyze how well the formal and informal aspects of a school’s social context are aligned. The focus is on two elementary schools engaged in initiatives aimed to use data to inform instructional decision making. The multimethod case study integrated findings from questionnaire and interview data. Data were collected over two years from case study schools. By fitting multilevel social selection models to longitudinal social network data collected from surveys, the authors estimated the relative influence of formal and informal processes on patterns of advice giving in each school. They used interview data to contextualize and corroborate findings. The social selection models they fit revealed distinct patt...

Meerkat is a tool for visualization and community mining of social networks. It is being developed to offer novel algorithms and functionality that other tools do not possess. Meerkat's features include navigation through graphical representations of networks, network querying and filtering, a multitude of graphical layout algorithms, community mining using recently developed algorithms, and dynamic network event analysis using recently published algorithms. These features will allow more insightful exploratory analysis and more robust inferences about communities and the significance of entity relationships. Meerkat is under active development, and future features will include additional options for community mining and visualization, focusing on algorithms and user interface designs not existing in other social network analysis tools.

This study presents an initial computational model of shared situation awareness (SA) based upon data collected from a simulated training exercise, designed to mimic real life events in a military personnel recovery center. Situation awareness was measured during the exercise using the Situation Awareness Global Assessment Technique (SAGAT). Our initial model examined how well five factors (social network distance, physical distance, rank similarity, branch similarity, and experience similarity) predicted shared SA. Overall, regression analyses highlighted the significant influence of geographical distribution (physical distance) on the development of shared SA and frequency of communications amongst team members. The discussion centers on the need for developing useful technological tools and techniques for supporting communication and collaboration among distributed teams.

Inter-county flows of commuters have long been used by the Bureau of the Census to identify MSAs and by the BEA to identify its Economic Areas. This paper looks at U.S. interregional flows of commuters, population, and goods in an effort to identify broader patterns of relationships among U.S. regions. A region’s primary flow up the central place hierarchy is found using tools commonly employed in Social Network Analysis. The results allow classification of regions in two ways: 1) as levels in a hierarchy; or 2) as a member of a group of regions all tied to the same member of the next-highest level of the hierarchy.

Individuals in social groups interact with numerous other group members in a polyadic network. Interactions can depend on the individual's own attributes (age, sex, status etc.), on their partner's attributes, and the group's network of social interactions. Previous studies tend to look at a subset of dyadic interactions, focusing on particular classes of individuals. We used social network analysis to explore how an individual wild meerkat's (Suricata suricatta) attributes related to their positions in three different interaction networks (grooming, dominance interactions, and foraging competitions) across eight groups. We asked whether individuals within groups associated assortatively and whether individuals with similar attributes occupied similar network positions. Differences in an individual's attributes did not consistently influence association patterns across different interaction network types. However, within network types, some attributes were especially influential across all groups. Grooming networks revealed negative assortativity by age and mass. Dominance networks revealed dominant-subordinate associations and high assortativity between males. Dominant individuals exhibited higher levels of dominance interactions and were aggressive to more different individuals than subordinates. Heavier individuals received higher levels of dominance interactions. Foraging competition networks revealed that younger and lighter individuals received higher overall levels of competitions and from more group members. Our observations were similar to focused studies on dyadic interactions but also revealed subtle differences. Future descriptions of social interactions should account for networks of social interactions occurring within a group and should be cautious about treating individuals with similar attributes as functionally similar with respect to their position within a social network.

Social Network Analysis (SNA) tries to understand and exploit the key features of social networks in order to manage their life cycle and predict their evolution. Increasingly popular web 2.0 sites are forming huge social network. Classical methods from social network analysis (SNA) have been applied to such online networks. In this paper, we propose leveraging semantic web technologies to merge and exploit the best features of each domain. We present how to facilitate and enhance the analysis of online social networks, exploiting the power of semantic social network analysis.

To examine the peer context of adolescent substance use, social network analysis was used to measure three domains of attributes of peer networks: social embeddedness, social status, and social proximity to substance users. The sample was a panel of 5,104 sixth, seventh, and eighth graders in three public school systems surveyed every 6 months for five assessments. Hierarchical generalized linear models showed that adolescents less embedded in the network, with greater status, and with closer social proximity to peer substance users were more likely to use substances. Also, adolescents in less dense networks and networks with higher smoking prevalence were more likely to smoke and use marijuana. Results establish the utility of social network analysis for measuring peer context and indicate that conventionality of relationships-having friends in the network, being liked but not too well liked, and having fewer friends who use substances-is most beneficial. Social network analysis has been identified as an appropriate method for studying the peer context of adolescent substance use (Ennett & Bauman,

The paper presents an exploratory study of the intra-organizational learning network in the context of a Spanish high-tech company. It expands the generalization of the network perspective to intra-organizational learning. Based on an exploratory social network analysis, we formulated four propositions that will be developed and contrasted in a later confirmatory study. First, the exploratory analysis demonstrates the importance of industry experience and tenure within the company as a common denominator of most central employees within learning networks. Second, similarity in terms of experiential level breeds mutual learning. Third, complementarity of knowledge is important for the formation of learning ties. Fourth, physical proximity creates opportunities to learn. Future research will need to test these four propositions in a confirmatory study.

In this case study we present an approach for using gamification elements to increase online student collaboration. In the study a gamified online discussion system was added to an introduction to programming course, with the aim of motivating the students to help each other. The actions in the discussion systems were analyzed and compared with user profiles and a student survey. The system had a positive impact on the course, increasing student collaboration, reduced response times and made course communications 88% more efficient by reducing email traffic.

Social capital is defined as the sum of interactions between both individuals and groups, which permit the development of rules, shared values, as well as cooperative and reciprocal links. This concept, developed by social science, can be applied as a support structure in decision making for natural resources. In particular, the quantification of social capital, divided into its respective component parts (social networks, associations and relationships with institutions) can be useful in the implementation of broad scale forest planning. The present work proposes a method to measure the structural social capital, together with forest planning field surveys, in order to highlight the potential to support participative decision making processes. The experimental implementation of the method was conducted in a mountainous area of South Italy, in the framework of an ongoing Forest Landscape Management Plan.

The Socio-Political Evaluation of Energy Deployment (SPEED) framework was proposed to improve understanding of energy technology deployment. It was intended to help energy policy-makers develop and implement more effective strategies to accelerate the deployment of emerging energy technologies. The theoretical underpinnings lie in the fields of sustainability science, political science, and risk perception. Part of the objectives of the SPEED framework are to identify the dominant socio-political influences on energy technology decisions and examine how policy can facilitate a societal response to climate change by contributing insights to stakeholders. The focus is at the state level because it is at the state level that emergent energy technologies are sited, permitted, and built. The purpose of this study was to examine the structure of communication about carbon capture and storage (CCS) technology from the perspective of individuals actively involved in decisions that affect deployment and diffusion. We use density of function-system networks to examine differences between states and categories stakeholders. The information is used to inform the discussion of the current structure of communication and how it might present either barriers or opportunities for CCS innovation.

We describe the design and evaluation of K-net, a social matching system to help people learn 'who knows what' in an organization by matching people with skills with those who need them. Transactive memory theory predicts that K-net will improve individuals' awareness of 'who knows what'. This should lead to improved performance through sharing knowledge across group boundaries. We evaluate K-net in terms of these predictions in an experiment with 41 students in seven groups working on software engineering projects. Accurate recommendations improved awareness of 'who knows what' versus 'random' recommendations, but did not improve performance. Our results highlight issues related to the evaluation of systems for sharing knowledge across group boundaries.

The goal of this research is to facilitate the design of systems which will mine and use sociocentric social networks without infringing privacy. We describe an extensive experiment we conducted within our organization comparing social network information gathered from various intranet public sources with social network information gathered from a private source -the organizational email system. We also report the conclusions of a series of interviews we conducted based on our experiment. The results shed light on the richness of public social network information, its characteristics, and added value over email network information.

Email is considered as one of the most widely accepted computer-mediated communication tools among university students. Evidence from the present literature shows that students make a significant amount of their course-related communications (e.g. discuss a topic with peers) using this tool. This study explores the dynamics of an email communication network, which was evolved among 34 university students throughout a semester, using measures of social network analysis and network simulation. These 34 students were doing a masters-degree course. They made 621 course-related email communications throughout the semester which consisted of 15 weeks including 13 semester-weeks, 1 week for mid-semester vacation and 1 week vacation before the final examination. From the analysis of this email communication network, it is found that: (i) students make an increased number of email communications with their peers at the end of the semester compared to the beginning of the semester; (ii) students’ communication network becomes sparse or decentralised over time during a semester; (iii) students have different levels of network participation at different times during a semester; and (iv) the reliabilities of the predictive power of reciprocity (i.e. an actor’s tendency of making reciprocal relations with other actors of the network), indegree-activity (i.e. effects of an actor’s present indegree on its future outdegree)  and outdegree-activity (i.e. effects of an actor’s present outdegree on its future outdegree) parameters of simulation models are changing significantly throughout the semester. Interpretations of these findings are also discussed in this paper.

Due to the continuous growth and spread of the internet using Web Mining to improve the quality of different services has become a necessity. Web Mining is nothing else than applying data mining techniques and algorithms on web data. In this work we present two algorithms used in Web Structure Mining namely Page Rank and HITS. Both algorithms draw their origin from social networks analysis and they are modeled based on the Theory of Markov Chains. Page Rank is used by the search engine GOOGLE and HITS by the search engine CLEVER. We present their strengths, weakness and other areas of applicability.

Benchmarking graph-oriented database workloads and graph-oriented database systems are increasingly becoming relevant in analytical Big Data tasks, such as social network analysis. In graph data, structure is not mainly found inside the nodes, but especially in the way nodes happen to be connected, i.e. structural correlations. Because such structural correlations determine join fan-outs experienced by graph analysis algorithms and graph

Visualization techniques have proven to be invaluable in crime analysis. By interviewing and observing Criminal Intelligence Officers (CIO) and civilian crime analysts at the Tucson Police Department (TPD), we identified two crime analysis tasks that can especially benefit from visualization: crime pattern recognition and criminal association discovery. As part of an extension to the COPLINK project [1], we have developed two systems to provide automatic visual assistance for these tasks. The Spatial-Temporal Visualization (STV) system assists in identifying crime patterns by integrating a synchronized view of three visualization techniques: a GIS view, a timeline view and a periodic pattern view. The Criminal Activities Network (CAN) system extracts, visualizes and analyzes criminal relationships using spring-embedded and blockmodeling algorithms. We present the functionalities of the STV and CAN systems in the demonstration section.

El interes del trabajo social y la intervencion social por el analisis de redes sociales aparece en los anos 70 con la fundacion de la INSNA. El analisis de redes sociales es una aproximacion con ciertas orientaciones teoricas singulares dentro de la sociologia y una metodologia especifica, cuyas expresiones mas comunes son el analisis de redes personales y el de redes completas. El analisis de redes sociales se ha aplicado especificamente en el campo del trabajo y la intervencion social. Concretamente, esta aproximacion ha dado sus frutos en el area del apoyo social, ya se trate de apoyo social informal, institucionalizado, o mixto, pero tambien al adaptarse las herramientas del ARS a la intervencion social. Ademas, esta perspectiva es prometedora en cuanto a nuevos usos futuros para el trabajo y la intervencion social, tanto mas en la medida en que se integren mejor sus herramientas.

A social network is a set of people (or organizations or other social entities) connected by a set of social relationships, such as friendship, co-working or information exchange. Social network analysis focuses on the analysis of patterns of relationships among people, organizations, states and such social entities. Social network analysis provides both a visual and a mathematical analysis of human relationships. Web can also be considered as a social network. Social networks are formed between Web pages by hyperlinking to other Web pages. In this paper a state of the art survey of the works done on social network analysis ranging from pure mathematical analyses in graphs to analyzing the social networks in Semantic Web is given. The main goal is to provide a road map for researchers working on different aspects of Social Network Analysis.

Formal and informal organization structure have been described as “opposing poles of a duality”—one which has yet to be fully resolved. The aim of this paper is to detail an approach to treating both structures in a comparable way, an approach that explicitly recognizes core and underlying commonalties. Two distinct  analytical strategies are employed to this end. The first involves a detailed comparison and contrast of selected and relevant structural dimensions drawn from the two research streams. The second strategy involves the application of these dimensions to the study of network graphs of both the formal and informal structures of the same organization at the same point in time.

ABSTRACT The increasing achievement of the Web has led people to exploit collaborative technologies in order to encourage partnerships among different groups. The cooperation can be achieved by Virtual Social Networks that facilitate people’s social interaction and enable them to remain in touch with friends exploiting the pervasive nature of information devices and services. The interest in analysing Virtual Social Networks has grown massively in recent years, and it involves researches from different fields. This led to the development of different methods to study relationships between people, groups, organisations- and other knowledge-processing entities on the Web. This chapter classifies these methods in two categories. The first category concerns methods used for the network data collection while the second category deals with methods used for the network data visualisation. The chapter gives an example of application of these methods to analyse the Virtual Social Network LinkedIn.

Social Network Analysis (SNA) is now a commonly used tool in criminal investigations, but evidence gathering and analysis is often restricted by data privacy laws. We consider the case where multiple investigators want to collaborate but do not yet have sufficient evidence that justifies a plaintext data exchange. We propose a practical solution that allows an investigator to expand his current view without actually exchanging sensitive private information. The investigator gets a partially anonymized view of the entire social network, while preserving his known view.

Primate social systems are difficult to characterize, and existing classification schemes have been criticized for being overly simplifying, formulated only on a verbal level or partly inconsistent. Social network analysis comprises a collection of analytical tools rooted in the framework of graph theory that were developed to study human social interaction patterns. More recently these techniques have been successfully applied to examine animal societies. Primate social systems differ from those of humans in both size and density, requiring an approach that puts more emphasis on the quality of relationships. Here, we discuss a set of network measures that are useful to describe primate social organization and we present the results of a network analysis of 70 groups from 30 different species. For this purpose we concentrated on structural measures on the group level, describing the distribution of interaction patterns, centrality, and group structuring. We found considerable variability in those measures, reflecting the high degree of diversity of primate social organizations. By characterizing primate groups in terms of their network metrics we can draw a much finer picture of their internal structure that might be useful for species comparisons as well as the interpretation of social behavior.

Recent years have witnessed the rapid proliferation and widespread adoption of a new class of information technologies, commonly known as social media. Researchers often rely on social network analysis (SNA) in attempting to understand these technologies, often without considering how the novel capabilities of social media platforms might affect the underlying theories of SNA, which were developed primarily through studies of offline social networks. This article outlines several key differences between traditional offline social networks and online social media networks by juxtaposing an established typology of social network research with a well-regarded definition of social media platforms that articulates four key features. The results show that at four major points of intersection, social media has considerable theoretical implications for SNA. In exploring these points of intersection, this study outlines a series of theoretically distinctive research questions for SNA in social media contexts. These points of intersection offer considerable opportunities for researchers to investigate the theoretical implications introduced by social media and lay the groundwork for a robust social media agenda potentially spanning multiple disciplines.

A number of empirical studies provide evidence for the importance of networks concerning innovation. Besides this, the relevance of the spatial component in this context is confirmed by the studies on localized knowledge spillovers, which found the knowledge diffusion often concentrated around the source of its generation. But these analyses are lacking insights in the networks the benefits for innovation resulting from.

dom, as conceived here) may be why his acknowledgement that we draw life and meaning from our participation in institutions does not lead him to reconceptualize the individual. We draw individuality from a personal and unique constellation that includes (but is not limited to) memberships of different kinds of institutions-not all of which have the same end-as well as different relationships with other individuals. It is not only or always the need to resist institutions that nourishes individuality-nor is individuality so free, thoughtful, or socially unconstituted as Bailey would personify it.

dom, as conceived here) may be why his acknowledgement that we draw life and meaning from our participation in institutions does not lead him to reconceptualize the individual. We draw individuality from a personal and unique constellation that includes (but is not limited to) memberships of different kinds of institutions-not all of which have the same end-as well as different relationships with other individuals. It is not only or always the need to resist institutions that nourishes individuality-nor is individuality so free, thoughtful, or socially unconstituted as Bailey would personify it.

Abstract. In this project we compare the success of startup entrepreneurs and innovators with their social networking behavior. In particular, we analyze the LinkedIn, Facebook, and e-Mail network of swissnex, Switzerland’s science and technology outpost in Boston connecting Swiss and American entrepreneurs and academics for collaborative work. We invited 500 members of the swissnex community to share their networking data, leading to 72 LinkedIn and 31 Facebook ego networks. We also included one month of all e-mail traffic of swissnex. We find that centrality in the network predicts entrepreneurial and academic success: the more central actors are in the different types of networks, the more successful they are. We also include the metric of “proximity ” to key people, including the swissnex director as well as a longtime Boston entrepreneur and academic. We again find that proximity in the LinkedIn network to these two people correlates with business and academic success of the me...

Social networks produce an enormous quantity of data. Facebook consists of over 400 million active users sharing over 5 billion pieces of information each month. Analyzing this vast quantity of unstructured data presents challenges for software and hardware. We present GraphCT, a Graph Characterization Toolkit for massive graphs representing social network data. On a 128processor Cray XMT, GraphCT estimates the betweenness centrality of an artificially generated (R-MAT) 537 million vertex, 8.6 billion edge graph in 55 minutes and a realworld graph (Kwak, et al.) with 61.6 million vertices and 1.47 billion edges in 105 minutes. We use GraphCT to analyze public data from Twitter, a microblogging network. Twitter's message connections appear primarily tree-structured as a news dissemination system. Within the public data, however, are clusters of conversations. Using GraphCT, we can rank actors within these conversations and help analysts focus attention on a much smaller data subset.

Studies about the influence and impact of knowledge-producing organisations' (e.g., universities or R&D institutions) have been addressed by means of strict economic analysis, stressing their economic impact in a local, regional or national extent. In the present study, an alternative methodology is put forward in order to evaluate the international scientific impact and influence of a knowledge-producing and-diffusing institution. Two main branches of the literature, dealing with the measurability of the economic impact of R&D organisations and their knowledge flows, are surveyed: standard economic studies, and scientometrics and bibliometrics analyses. With the aim of implementing an approach closer to the latter methods, we map the international scientific network of a knowledge-producing organisation using social network analysis statistical tools and thus assess the international impact and influence of such institution. Using INESC Porto-a Portuguese private non-profit organisation, recognised as being of public interest, due to its R&D activities-as our case study, we analyse the dynamics of its scientific production over the last two decades, attributing special attention to the international scientific co-authorships' evolution, outlining the architecture of the knowledge network and its changing pattern. Additionally, focusing on the most prolific scientific areas of INESC Porto, and resorting to published scientific work recorded in the Science Citation Index (SCI) of the Institute for Scientific Information (ISI), we trace citations and infer over INESC Porto's international scientific influence and impact.