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

Contextual sub-network extraction in contextual social networks

Predicting the trust between a source participant and a target participant in a social network is important in many applications, e.g., assessing the recommendation from a target participant from the perspective of a source participant. In general, social networks contain participants, the links and trust relations between them and the contextual information for their interactions. All such information has important influence on trust prediction. However, predicting the trust between two participants based on the whole network is ineffective and inefficient. Thus, prior to trust prediction, it is necessary to extract a small-scale contextual network that contains most of the important participants as well as trust and contextual information. However, extracting such a sub-network has been proved to be an NP-Complete problem. To solve this challenging problem, we propose a social context-aware trust sub-network extraction model to search near-optimal solutions effectively and efficiently. In our proposed model, we first present the important factors that affect the trust between participants in OSNs. Then, we define a utility function to measure the trust factors of each node in a social network. At last, we design an ant colony algorithm with a newly designed mutation process for sub-network extraction. The experiments, conducted on two popular datasets of Epinions and Slashdot, demonstrate that our approach can extract those sub-networks covering important participants and contextual information while keeping a high density rate. Our approach is superior to the state-of-the-art approaches in terms of the quality of extracted sub-networks within the same execution time.8 page(s