Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks

Trust can be defined as a measure to determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this dissertation, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs. We discuss three main challenges in this domain and present novel trust prediction approaches to address them. We first focus on proposing a low-rank representation of users that incorporates users' personality traits as additional information. Then, we propose a set of context-aware trust prediction models. Finally, by considering the time-dependency of trust relations, we propose a dynamic deep trust prediction approach. We design and implement five pair-wise trust prediction approaches and evaluate them with real-world datasets collected from OSNs. The experimental results demonstrate the effectiveness of our approaches compared to other state-of-the-art pair-wise trust prediction models.Comment: 158 pages, 20 figures, and 19 tables. This is my PhD thesis in Macquarie University, Sydney, Australi

Detection of illicit behaviours and mining for contrast patterns

This thesis describes a set of novel algorithms and models designed to detect illicit behaviour. This includes development of domain specific solutions, focusing on anti-money laundering and detection of opinion spam. In addition, advancements are presented for the mining and application of contrast patterns, which are a useful tool for characterising illicit behaviour. For anti-money laundering, this thesis presents a novel approach for detection based on analysis of financial networks and supervised learning. This includes the development of a network model, features extracted from this model, and evaluation of classifiers trained using real financial data. Results indicate that this approach successfully identifies suspicious groups whose collaborative behaviour is indicative of money laundering. For the detection of opinion spam, this thesis presents a model of reviewer behaviour and a method for detection based on statistical anomaly detection. This method considers review ratings, and does not rely on text-based features. Evaluation using real data shows that spammers are successfully identified. Comparison with existing methods shows a small improvement in accuracy, but significant improvements in computational efficiency. This thesis also considers the application of contrast patterns to network analysis and presents a novel algorithm for mining contrast patterns in a distributed system. Contrast patterns may be used to characterise illicit behaviour by contrasting illicit and non-illicit behaviour and uncovering significant differences. However, existing mining algorithms are limited by serial processing making them unsuitable for large data sets. This thesis advances the current state-of-the-art, describing an algorithm for mining in parallel. This algorithm is evaluated using real data and is shown to achieve a high level of scalability, allowing mining of large, high-dimensional data sets. In addition, this thesis explores methods for mapping network features to an item-space suitable for analysis using contrast patterns. Experiments indicate that contrast patterns may become a valuable tool for network analysis