Trust Model for Social Network using Singular Value Decomposition

For effective interactions to take place in a social network, trust is important. We model trust of agents using the peer to peer reputation ratings in the network that forms a real valued matrix. Singular value decomposition discounts the reputation ratings to estimate the trust levels as trust is the subjective probability of future expectations based on current reputation ratings. Reputation and trust are closely related and singular value decomposition can estimate trust using the real valued matrix of the reputation ratings of the agents in the network. Singular value decomposition is an ideal technique in error elimination when estimating trust from reputation ratings. Reputation estimation of trust is optimal at the discounting of 20 %

Modeling Social Media User Content Generation Using Interpretable Point Process Models

In this article, we study the activity patterns of modern social media users on platforms such as Twitter and Facebook. To characterize the complex patterns we observe in users' interactions with social media, we describe a new class of point process models. The components in the model have straightforward interpretations and can thus provide meaningful insights into user activity patterns. A composite likelihood approach and a composite EM estimation procedure are developed to overcome the challenges that arise in parameter estimation. Using the proposed method, we analyze Donald Trump's Twitter data and study if and how his tweeting behavior evolved before, during and after the presidential campaign. Additionally, we analyze a large-scale social media data from Sina Weibo and identify interesting groups of users with distinct behaviors; in this analysis, we also discuss the effect of social ties on a user's online content generating behavior

Graphical Models in Characterizing the Dependency Relationship in Wireless Networks and Social Networks

Semi-Markov processes have become increasingly important in probability and statistical modeling, which have found applications in traffic analysis, reliability and maintenance, survival analysis, performance evaluation, biology, DNA analysis, risk processes, insurance and finance, earthquake modeling, etc. In the first part of this thesis, our focus is on applying semi-Markov processes to modeling the on-off duty cycles of different nodes in wireless networks. More specifically, we are interested in restoration of statistics of individual occupancy patterns of specific users based on wireless RF observation traces. In particular, we present a novel approach to finding the statistics of several operations, namely down-sampling, superposition and mislabelling, of a discrete time semi-Markov process in terms of the sojourn time distributions and states transition matrix of the resulting process. The resulting process, after those operations, is also a semi-Markov processes or a Markov renewal process. We show that the statistics of the original sequence before the superposition operation of two semi Markov processes can be generally recovered. However the statistics of the original sequence cannot be recovered under the down-sampling operation, namely the probability transition matrix and the sojourn time distribution properties are distorted after the down-sampling. Simulation and numerical results further demonstrate the validity of our theoretical findings. Our results thus provide a more profound understanding on the limitation of applying semi-Markov models in characterizing and learning the dynamics of nodes\u27 activities in wireless networks. In the second portion of the thesis a review is provided about several graphical models that have been widely used in literature recently to characterize the relationships between different users in social networks, the influence of the neighboring nodes in the networks or the semantic similarity in different contexts