LEARNING WITH SPARSITY FOR DETECTING INFLUENTIAL NODES IN IMPLICIT INFORMATION DIFFUSION NETWORKS

The diffusion of information and spreading influence are ubiquitous in social networks. How to model and extract useful information from diffusion networks especially in social media domain is still an open research area that requires significant attention. Many real applications pose new challenges in modeling information diffusion process. In particular, the first challenge comes from the fact that the underlying network structure over which the propagation spreads is unknown or unobserved. It is often the case that one can only observe that when nodes got infected by which contagion but without the knowledge about who infecting whom. The second challenge comes from the simultaneous transmissions of multiple correlated contagions through an implicit network. The third one comes from strong temporal effect in the diffusion process which needs to be carefully modeled. In my thesis, we address two fundamental tasks, forecasting and influential-node detection, in an implicit diffusion network by a unified approach. In particular, we first proposed a sparse linear influence model (SLIM) which takes a nice form of a convex optimization problem. We further extended SLIM to multi-task sparse linear influence model (MSLIM), which could model diffusion networks with multiple correlated contagions. MSLIM, as a richer model than SLIM, not only improves prediction accuracy, but also allows to select influential nodes on a finer grid, i.e., select different sets of influential nodes for different contagions. For SLIM and MSLIM, we developed both deterministic and stochastic optimization algorithms for solving the corresponding problems and showed the fast theoretical convergence guarantees. Another contribution of the thesis is the development of a general purpose system, called Slow Intelligent System (SIS), which is able to continuously learn and improve performance over time. We proposed the component-based SIS and developed the software with applications to face recognition task. Furthermore, we utilized the idea of the SIS to systematize the information diffusion process modeling and influential node detection and proposed SIS-based SLIM/MSLIM approaches, which further improve the flexibility and scalability of learning from implicit diffusion networks. We demonstrated the superiority of the proposed approaches on several real datasets from social media domains

Internet and Smartphone Use-Related Addiction Health Problems: Treatment, Education and Research

This Special Issue presents some of the main emerging research on technological topics of health and education approaches to Internet use-related problems, before and during the beginning of coronavirus disease 2019 (COVID-19). The objective is to provide an overview to facilitate a comprehensive and practical approach to these new trends to promote research, interventions, education, and prevention. It contains 40 papers, four reviews and thirty-five empirical papers and an editorial introducing everything in a rapid review format. Overall, the empirical ones are of a relational type, associating specific behavioral addictive problems with individual factors, and a few with contextual factors, generally in adult populations. Many have adapted scales to measure these problems, and a few cover experiments and mixed methods studies. The reviews tend to be about the concepts and measures of these problems, intervention options, and prevention. In summary, it seems that these are a global culture trend impacting health and educational domains. Internet use-related addiction problems have emerged in almost all societies, and strategies to cope with them are under development to offer solutions to these contemporary challenges, especially during the pandemic situation that has highlighted the global health problems that we have, and how to holistically tackle them