Graph-based Analytics for Decentralized Online Social Networks

Decentralized Online Social Networks (DOSNs) have been introduced as a privacy preserving alternative to the existing online social networks. DOSNs remove the dependency on a centralized provider and operate as distributed information management platforms. The main objective behind decentralization is to preserve user privacy in both shared content and communication. Current efforts of providing DOSNs are mainly focused on designing the required building blocks for managing the distributed network and supporting the social services (e.g., search for topics or people, content delivery, etc.). However, there is a lack of reliable techniques for enabling complex analytical services (e.g., spam detection, identity validation, recommendation systems, etc.) that comply with the decentralization requirements of DOSNs. In particular, there is a need for decentralized data analytic techniques and machine learning (ML) algorithms that can successfully run on top of DOSNs. In this thesis, we empower decentralized analytics for DOSNs through a set of novel algorithms. Our algorithms allow decentralized analytics to effectively work on top of fully decentralized topology, when the data is fully distributed and nodes have access to their local knowledge only. Additionally, our algorithms follow unsupervised ML paradigm, thus removing the need for collecting labeled training data that potentially puts user privacy at risk. Furthermore, our algorithms and methods are able to extract and exploit the latent patterns in the social user interaction networks and effectively combine them with the shared content, yielding significant improvements for the complex analytic tasks. We argue that, community identification is at the core of the learning and analytical services provided for DOSNs. We show in this thesis that knowledge on community structures and information dissemination patterns, embedded in the topology of social networks has a potential to greatly enhance data analytic insights and improve results. At the heart of this thesis lies a community detection technique that successfully extracts communities in a completely decentralized manner. In particular, we show that multiple complex analytic tasks, like spam detection and identity validation, can be successfully tackled by harvesting the information from the social network structure. This is achieved by using decentralized community detection algorithm which acts as the main building block for the community-aware learning paradigm that we lay out in this thesis. To the best of our knowledge, this thesis represents the first attempt to bring complex analytical services, which require decentralized iterative computation over distributed data, to the domain of DOSNs. The experimental evaluation of our proposed algorithms using real-world datasets confirms the ability of our solutions to generate efficient ML models in massively parallel and highly scalable manner. Furthermore, our algorithms preserve user privacy and achieve better performance compared to the existing centralized and global approaches.