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).

Includes bibliographical references (p. 105-117).

Information propagation has attracted increasing attention from sociologists, marketing researchers and Information Technology entrepreneurs. With the rapid developments in online and mobile social applications like Facebook, Twitter, and LinkedIn, large-scale, high-speed and instantaneous information dissemination becomes possible, spawning tremendous opportunities for electronic commerce. It is non-trivial to make an accurate analysis on how information is propagated due to the uncertainty of human behavior and the complexity of the social environment. This dissertation is concerned with exploring models, formulations, and heuristics for the social-network-based information propagation process. It consists of three major parts: information diffusion through online social network, modeling social influence propagation, and social-network-based information spreading in opportunistic mobile networks. Firstly, I consider the problem of maximizing the influence propagation through online social networks. To solve it, I introduce a probabilistic maximum coverage problem, and propose a cluster-based heuristic and a neighbor-removal heuristic for two basic diffusion models, namely, the Linear Threshold Model and the Independent Cascade Model, respectively. Realizing that the selection of influential nodes is mainly based on the accuracy and efficiency in estimating the social influence, I build a framework of up-to-2-hop hierarchical network to approximate the spreading of social influence, and further propose a hierarchy-based algorithm to solve the influence maximization problem. Our heuristic is proved to be efficient and robust with competitive performance, low computation cost, and high scalability. The second part explores the modeling on social influence propagation. I develop an analytical model for the influence propagation process based on discrete-time Markov chains, and deduce a close-form equation to express the n-step transition probability matrix. We show that given any initial state the probability distribution of the converged network state could be easily obtained by calculating a matrix product. Finally, I study the social-network-based information spreading in opportunistic mobile networks by analyzing the opportunistic routing process. I propose three social-network-based communication pattern models and utilize them to evaluate the performance of different social-network-based routing protocols based on several human mobility traces. Moreover, I discuss the fairness evaluation in opportunistic routing, and propose a fair packet forwarding strategy which operates as a plugin for traditional social- network-based routing protocols. My strategy improves the imbalance of success rates among users while maintaining approximately the same system throughput. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Infections frequently occur on both networks of devices and networks of people, and can model not only viruses, but also information, rumors, and product use. However, in many circumstances, the infection process itself is hidden, and only the effects, e.g. sickness or knowledge, can be observed. In addition, this information is likely incomplete, missing many sick nodes, as well as inaccurate, with false positives. To use this data effectively, it is often essential to identify the infection process causing the sickness, or even whether the cause is an infection. For our purposes, we consider the susceptible-infected (SI) infection model. We seek to distinguish between infections and random sickness, as well as between different infection (or infection-like) processes in a limited information setting. We formulate this as a hypothesis testing problem, where (typically) in the null, the sickness affects nodes at random, and in the alternative, the infection is spread through the network. Similarly, we consider the case where the sickness may be caused by one of two infection (or infection-like) processes, and we wish to find which is the causative process. We do this is a setting with very limited information, given only a single snapshot of the infection. Only a small portion of the infected population reports the sickness. In addition, there are several other limitations we consider. There may be false positives, obfuscating the infection. Similarly, there may be a random sickness and epidemic process occurring simultaneously. Knowledge of the graph topology may be incomplete, with unknown edges over which the infection may spread. The graph may also be weighted, affecting the way the infection spreads over the graph. In all these cases, we develop algorithms to identify the causative process of the infection utilizing the fact that infected nodes will be clustered. We demonstrate that under reasonable conditions, these algorithms detect an infection with asymptotically zero error probability as the graph size increases. / text

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