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No description available.

As people go about their lives, they form a variety of social relationships, such as family, friends, colleagues, and acquaintances, and these relationships differ in their strength, indicating the level of trust among these people. The trend in these relationships is for people to trust those who they have met in real life more than unfamiliar people whom they have only met online. In online social network sites the objective is to make it possible for users to post information and share albums, diaries, videos, and experiences with a list of contacts who are real-world friends and/or like-minded online friends. However, with the growth of online social services, the need for identifying trustworthy people has become a primary focus in order to protect users’ vast amounts of information from being misused by unreliable users. In this thesis, we introduce the Capacity- first algorithm for identifying a local group of trusted people within a network. In order to achieve the outlined goals, the algorithm adapts the Advogato trust metric by incorporating weighted social relationships. The Capacity-first algorithm determines all possible reliable users within the network of a targeted user and prevents malicious users from accessing their personal network. In order to evaluate our algorithm, we conduct experiments to measure its performance against other well-known baseline algorithms. The experimental results show that our algorithm’s performance is better than existing alternatives in finding all possible trustworthy users and blocking unreliable ones from violating users’ privacy.

Online Social Networks (OSNs) have seen an enormous boost in popularity in recent years. Along with this popularity has come tribulations such as privacy concerns, spam, phishing and malware. Many recent works have focused on automatically detecting these unwanted behaviors in OSNs so that they may be removed. These works have developed state-of-the-art detection schemes that use machine learning techniques to automatically classify OSN accounts as spam or non-spam. In this work, these detection schemes are recreated and tested on new data. Through this analysis, it is clear that spammers are beginning to evade even these detectors. The evasion tactics used by spammers are identified and analyzed. Then a new detection scheme is built upon the previous ones that is robust against these evasion tactics. Next, the difficulty of evasion of the existing detectors and the new detector are formalized and compared. This work builds a foundation for future researchers to build on so that those who would like to protect innocent internet users from spam and malicious content can overcome the advances of those that would prey on these users for a meager dollar.

This research describes the relationship between several social network characteristics and knowledge creation outputs in the form of patented intellectual property of researchers by investigating the case of the University of Waterloo. Based on a literature review in the domains of social networks and knowledge creation, this research focuses on the position of knowledge creation between social closure theory and structural hole theory. These are the two seminal theories of the creation of social capital through social networks. From this body of literature, this thesis develops the research question involving five hypotheses. These hypotheses test whether network density, strength of relationships, diversity of relationships, and amount of research funding have a positive correlation with the number of patents held by the researcher, and whether network size has a negative correlation with number of patents held by a researcher. The data for this research comes from a variety of secondary sources including the University's Office of Research, UWDIR online directory, NSERC research awards search engine, and CIPO patent database. Using a combination of social network analysis and statistical regression analysis, this research shows that network density, diversity of relationships, and amount of research funding have a positive correlation with knowledge creation outputs, while network size has a negative relationship with knowledge creation outputs. Understanding the relationship that these social network factors have with the knowledge creation outputs can help the University develop strategies to help improve their knowledge creation processes, thereby putting the University in a stronger position to facilitate the development of patentable ideas and innovations by encouraging the development of research centres and institutes that intersect disciplinary boundaries.

Thesis (M.A.) - Simon Fraser University, 2005. / Theses (Faculty of Education) / Simon Fraser University. Also issued in digital format and available on the World Wide Web.

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (p. 109-126). Also available in print.

Thesis (Ph. D.)--Drexel University, 200. / Includes abstract and vita. Includes bibliographical references (leaves 53-60).

Thesis (Ph. D.)--York University 2001. Graduate Programme in Psychology. / Typescript. Includes bibliographical references (leaves 187-209). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNQ66366.

Includes bibliographical references (p. 105-117).