Abstract It likes a knowledge ¡§Yellow Pages ¡¨, knowledge map, indicates where is knowledge and how to get it, but doesn¡¦t contain knowledge. The principal purpose of a knowledge map is showing domain expert when someone need expertise. The resources of teachers¡¦ knowledge map, teachers¡¦ professional information, are fragmented by geographic condition. The map is piece not complete one. As rapid development of Internet, the rich webs contents provide a new way to build global teachers¡¦ knowledge map. The goal of this research is constructing¡yTeachers¡¦ Knowledge Map¡zfor sufficient knowledge sharing environment by collecting teachers¡¦ relative information from the web pages automatically and integrating plentiful Internet resource. There are four main purposes of this research, include (1) getting teachers¡¦ vita from web gages. (2) using teachers¡¦ personal vita and others webs¡¦ resources to construct teacher¡¦s professional specialty, and indicate research issues of teachers. (3) reflecting teachers¡¦ social network by web pages to show social information of individual teacher or group. Teachers¡¦ social network can provide information of how to get the expertise. (4) integrating prior purposes to create useful teachers¡¦ knowledge map for sufficient knowledge sharing environment.

Social network analysis is used to find all relationships from the group, dig out the prominent patterns, and observe how information flows between dyads. By social network analysis approaches, users can know how information flows through network ties, how people acquire information and resources, and how cleavages and coalitions operate. In this research, we develop a useful social network analysis system to facilitate teams¡¦ collaboration. The system can draw a social network in ego-centered or whole network layout, and provide information of social network attributes of all users. Both team leaders and general members can make use of it to understand relations and interaction patterns of their team. We also generalize social network attributes to analyze task-based teams at different team development stages for discovering the interaction patterns of different groups in groups¡¦ life cycles. Interaction patterns of members in the team and roles that users play have high influence on a virtual team¡¦s development. With these discoveries, team leaders can obtain concise information about their teams¡¦ performance, and community managers can capture stereotypes of virtual teams in the community. From these evaluation results, we confirm that social network analysis is a useful means to analyze the knowledge activities conducted by virtual teams.

Facebook is the dominant SNS for American students in the United States, and Renren.com is heavily used by Chinese students in China. Chinese international students in the United States are likely to use both the host and home SNSs to keep in touch with their friends in the host and home countries. The purpose of the study is to explore the similarities and differences between host and home SNS use among Chinese international students in the U.S. This study compares their use of Facebook and Renren.com with respect to intensity and patterns of use. It explores how these student sojourners in the U.S. use the two SNSs to build up and maintain their social networks and social capital and how their levels of acculturation to American host culture and maintenance of Chinese home culture are associated with their SNS use. Quantitative data collected through a survey of 212 Chinese international students at the University of Texas at Austin was analyzed to address these research questions. The findings suggest that Chinese international students use Renren.com more intensively than Facebook and prefer Renren.com to Facebook for the purposes of communication and information seeking. They are more likely to use Renren.com than Facebook to interact with Chinese friends whether in the U.S., in China, or in other parts of the world. The intensity of Facebook and Renren.com use were found to be positively associated with bridging social capital, but neither of the two is associated with bonding social capital. Only the intensity of Renren.com use was found to have a positive relationship with maintained social capital. Furthermore, the levels of acculturation to host culture are associated with the intensity of Facebook use, while the levels of maintenance to home culture are associated with the intensity of Renren.com use. / text

Main thesis objective is in social network analysis. Theoretic will describe their origin, development and circumstances under which certain social networks were built. Part with analysis will concern in how to compete with business rivals using CI and will search techniques for proper facebook usage as a company's CI tool by rating of chosen fan facebook pages.

Analysis of Inferred Social Networks While the social network analysis (SNA) is not a new science branch, thanks to the boom of social media platforms in recent years new methods and approaches appear with increasing frequency. However, not all datasets have network structure visible at first glance. We believe that every reasonable interconnected system of data hides a social network, which can be inferred using specific methods. In this thesis we examine such social network, inferred from the real-world data of a smaller bank. We also review some of the most commonly used methods in SNA and then apply them on our complex network, expecting to find structures typical for traditional social networks.

Voluntary turnover is an important organizational issue with costs beyond monetary losses (Morrow & McElroy, 2007). Subsequently, the detrimental effects have engendered extensive research that has led to multiple turnover models attempting to unite antecedents to maximize the variance in predicting turnover and turnover intent (Griffeth et al., 2000). However, current models have omitted important aspects of an employee’s working experience. This dissertation addresses that gap; namely, the need to incorporate relational forces at work that keep individuals at their current organizations. The study integrates social relations and the traditional turnover model (Mobley, 1977) to examine the unique and joint effects of social relations in predicting turnover intent. An empirical study of two independent samples of full-time working individuals (N = 318; N = 235) endorsed a mixed methods approach to expand the measurement of social relations by examining social network content, strength, structure, and influence. Select work personality traits, work characteristics, and turnover outcomes were assessed via an online questionnaire. The results demonstrate that expressive link defection (i.e., friends leaving the organization), instrumental normative pressure to stay (i.e., advisors wanting employees to stay), and instrumental strength (i.e., frequency of contact with advisors) predict significant variance in turnover intent beyond traditional predictors. In addition, expressive link defection and instrumental normative pressure to stay had stronger relationships with turnover intent for longer tenured employees than shorter tenured employees.

This paper structurally estimates a dynamic model of usage behavior and network effects in a social network site using data from MySpace.com. We view a social network as a stock of capital that yields a flow of utilities over time by creating social interactions between the owner and her friends. When one decides to use a social network site, it may have two distinct network effects: (1) one can manage an existing base of friends through social networking and thus prevent depreciation of capital stock (maintenance effect), and (2) one may acquire new friends through social networking, which results in creation of new capital stock (investment effect). Thus, we model social networking as a dynamic process, in which one's current action to use a social network site can influence the evolution of her social network. We found that realtime chat and messaging, features of MySpace.com, positively affect one's usage decision and hence achieve the intended goal of generating site traffic. However, different demographic groups may have idiosyncratic preferences for these features. Based on parameter estimates, we performed counterfactual simulations with the goal of providing managers with ways to enhance firm performance. / text

Graph pattern matching is fundamental to social network analysis. Its effectiveness for identifying social communities and social positions, making recommendations and so on has been repeatedly demonstrated. However, the social network analysis raises new challenges to graph pattern matching. As real-life social graphs are typically large, it is often prohibitively expensive to conduct graph pattern matching over such large graphs, e.g., NP-complete for subgraph isomorphism, cubic time for bounded simulation, and quadratic time for simulation. These hinder the applicability of graph pattern matching on social network analysis. In response to these challenges, the thesis presents a series of effective techniques for querying large, dynamic, and distributively stored social networks. First of all, we propose a notion of query preserving graph compression, to compress large social graphs relative to a class Q of queries. We then develop both batch and incremental compression strategies for two commonly used pattern queries. Via both theoretical analysis and experimental studies, we show that (1) using compressed graphs Gr benefits graph pattern matching dramatically; and (2) the computation of Gr as well as its maintenance can be processed efficiently. Secondly, we investigate the distributed graph pattern matching problem, and explore parallel computation for graph pattern matching. We show that our techniques possess following performance guarantees: (1) each site is visited only once; (2) the total network traffic is independent of the size of G; and (3) the response time is decided by the size of largest fragment of G rather than the size of entire G. Furthermore, we show how these distributed algorithms can be implemented in the MapReduce framework. Thirdly, we study the problem of answering graph pattern matching using views since view based techniques have proven an effective technique for speeding up query evaluation. We propose a notion of pattern containment to characterise graph pattern matching using views, and introduce efficient algorithms to answer graph pattern matching using views. Moreover, we identify three problems related to graph pattern containment, and provide efficient algorithms for containment checking (approximation when the problem is intractable). Fourthly, we revise graph pattern matching by supporting a designated output node, which we treat as “query focus”. We then introduce algorithms for computing the top-k relevant matches w.r.t. the output node for both acyclic and cyclic pattern graphs, respectively, with early termination property. Furthermore, we investigate the diversified top-k matching problem, and develop an approximation algorithm with performance guarantee and a heuristic algorithm with early termination property. Finally, we introduce an expert search system, called ExpFinder, for large and dynamic social networks. ExpFinder identifies top-k experts in social networks by graph pattern matching, and copes with the sheer size of real-life social networks by integrating incremental graph pattern matching, query preserving compression and top-k matching computation. In particular, we also introduce bounded (resp. unbounded) incremental algorithms to maintain the weighted landmark vectors which are used for incremental maintenance for cached results.

This thesis studies social influence from the perspective of users' characteristics. The importance of users' characteristics in word-of-mouth applications has been emphasized in economics and marketing fields. We model a category of users called mavens where their unique characteristics nominate them to be the preferable seeds in viral marketing applications. In addition, we develop some methods to learn their characteristics based on a real dataset. We also illustrate the ways to maximize information flow through mavens in social networks. Our experiments show that our model can successfully detect mavens as well as fulfill significant roles in maximizing the information flow in a social network where mavens considerably outperform general influential users for influence maximization. The results verify the compatibility of our model with real marketing applications.

Individual variation in asocial and social behavioural traits can affect patterns of social association. Resultant individual-level variation in sociality can be quantified using social network analysis. Social network analysis has recently been applied to the study of the evolution and development of social behaviour. Though captive systems have provided useful contributions to this endeavour, investigating the factors shaping social structure in wild populations affords superior ecological relevance. The characterisation of the social structure of wild animals has been greatly aided by improvements in automated data collection methods, particularly the miniaturisation of Radio-Frequency Identification (RFID) technology for the purposes of studying the social foraging behaviour of wild birds. In this thesis, I use RFID methods to examine the factors influencing between-individual variation in foraging routines (Chapter Two) and social network position (Chapter Three) in wild populations of a colonial corvid species, the jackdaw (Corvus monedula). I then relate social network position to reproductive success (Chapter Three) and investigate the developmental plasticity of jackdaw social behaviour by determining the effect of early life conditions on social network position (Chapter Four). Finally, I describe the fine-scale temporal dynamics of social foraging, the nature of accompaniment during paired foraging and the foraging benefits of social support (Chapter Five).