Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Finding disjoint dense clubs in an undirected graph(Montana State University - Bozeman, College of Engineering, 2016) Zou, Peng; Chairperson, Graduate Committee: Binhai ZhuFor over a decade, software like Twitter, Facebook and WeChat have changed people's lives by creating social groups and networks easily. They give people a new convenient 'world' where we can share everything that happens around us, and social networks have grown enormously in recent years. In essence, social networks are full of data and have become an indispensable part of our life. Trust is an important feature of the relationship between two users in a social network. With the development of social networks, the trust among its members has become a big issue. In a social network, the trust among its members usually cannot be carried over many users. In the corresponding social network modeled as a graph, a user is denoted by a vertex and an edge between two vertices means that these two users communicate a lot above some threshold and they trust each other. An online social community is usually corresponding to a dense region in such a graph. A complex social network is usually composed of several groups/communities (the regions with a lot of edges), and this characterization of community structure means the appearance of densely connected groups of vertices, with only sparse connections between groups. For analyzing the structure of social networks and the relationship between users, it is important to find disjoint groups/communities with a small diameter and with a decent size, formally called dense clubs in this thesis. We focus on handling this NP-complete problem in this thesis. First, from the parameterized computational complexity point of view, we show that this problem does not admit a polynomial kernel (implying that it is unlikely to apply some reduction rules to obtain a practically small problem size). Then, we focus on the dual version of the problem, i.e., deleting 'd' vertices to obtain some disjoint dense clubs. We show that this dual problem admits a simple FPT algorithm using a bounded search tree method (the running time is still too high for practical datasets). Finally, we combine a simple reduction rule together with some heuristic methods to obtain a practical solution (verified by extensive testing on practical datasets). Empirical results show that this heuristic algorithm is very sensitive to all parameters. This algorithm is suitable on graphs which have a mixture of dense and sparse regions. These graphs are very common in the real world.Item Interpersonal trust measurements from social interactions in Facebook(Montana State University - Bozeman, College of Engineering, 2014) Li, Xiaoming; Chairperson, Graduate Committee: Qing YangInterpersonal trust is widely cited as an important component in several network systems such as peer-to-peer (P2P) networks, e-commerce and semantic web. However, there has been less research on measuring interpersonal trust due to the difficulty of collecting data that accurately reflects interpersonal trust. To address this issue, we quantify interpersonal trust by analyzing the social interactions between users and their friends on Facebook. Currently, friends of a user in almost all online social networks (OSN) are indistinguishable, i.e. there is no explicit indication of the strength of trust between a user and her close friends, as opposed to acquaintances. Existing research on estimating interpersonal trust in OSN faces two fundamental problems: the lacks of established dataset and a convincing evaluation method. In this thesis, we consider bidirectional interacting data in OSN to deconstruct a user's social behavior, and apply Principle Component Analysis (PCA) to estimate the interpersonal trust. A Facebook app, itrust, is developed to collect interaction data and calculate interpersonal trust. Moreover, we adopt the Kendall's tau and Generalized Kendall's tau methods to evaluate the accuracy of ranking list generated by itrust. Results show that itrust achieves more accurate interpersonal trust measurements than existing methods.Item An anytime algorithm for trust propagation in social networks(Montana State University - Bozeman, College of Engineering, 2011) Hamilton, Andrew Johnson; Chairperson, Graduate Committee: John SheppardTrust propagation in social networks is a challenging task. It is difficult to model human trust, and the data is huge and very sparse. Due to these challenges, the algorithms available to propagate trust have complexity issues. We used the MRFTrust algorithm created by Tosun and Sheppard to produce an anytime algorithm for trust propagation. To do this we use sampling techniques and increased horizon size to reduce the complexity and decrease runtimes. We show that we can dramatically reduce the number of nodes considered in the algorithm, yet still achieve a superior result.