THE THEORY AND KEY TECHNOLOGY RESEARCH IN SOCIAL NETWORKS

Currently social networks are the pulse of humanity. For there are the platforms on which people share content and form links among themselves for various purposes The traditional social networks that were formed by human relationships or activities have been supplanted with the online social networks (OSNs). And due to the availability of affordable and portable digital devices and also the emergence of the Internet and services offered by the Web 2.0, there have been a tremendous amount of data generated from the digital human activities on these sites. These digital human activities become complex in the process of time and thus practically infeasible to understand the nature of their underlying wiring (topology) and properties via observation or manual means. Due to this various algorithms have been developed and research done to help understand the network topology and properties of social networks and there have been some good findings. But there are still major challenges associated with the theory and key technology research in the field of social network study which this dissertation addresses as follows Firstly the lack of a comprehensive and a fi rm theoretical framework for the in-depth study and analysis of social network is a concern in the research community. We address this challenge by outlining a thorough and well-balanced presentation of the key network theoretical framework and used it to carry out some novel network-centric analysis on the social and other complex networks studied in this dissertation. Secondly, we develop algorithms and use the network theoretical tools outlined in this thesis to analyze the structural and spectral properties of social networks Visual presentations of the correlation of their structural and spectral properties are made and various novel results are presented . These results present a better perspective for modelling real life networks than the one-dimensional methods used in the literature . Thirdly, the properties of social networks are not well applied in other areas of research . But social network structural and spectral properties contain rich data that can be leveraged for modeling real life system. s To this end, we apply the properties of social network studied in this dissertation to model three real life situations. They include; Human disease modeling: Most of the epidemic models in literature used the random network model to simulate the epidemic spreading in human society. However, human society is not random in many aspect , s as such these results do not give a true picture of human disease spreading. Social networks have a high degree of resemblance to human society , as such, we leveraged their properties to model epidemic spreading and reported novel findings

•Modeling information spreading or maximization: We develop an information maximization algorithm that scale with the size of the network and use it to model a novel information or influence maximization system and reported results that are better than the state-of-the-art algorithms that used greedy approach. • To infer and measure trust: The graph-theoretical properties of social networks can infer and measure trust more accurately than the current state of the art systems. For instance, OSNs are known to possess a tight core that has a high level of reciprocity among the user. s Users within the core are reached via many short paths, and thus a malicious user would be hardly trusted unless she is able to penetrate the core by skewing many short paths and thus appearing trustworthy to a larger percentage of the core user. s We use these analytical insights to propose a trust metric and use it to measure trust levels on 4 OSN s and reported various novel findings that can serve as a benchmark for inferring trust on an unknown user. The significance of these findings is enormous . These results would be of interest to health and governmental institutions on how they can leverage on the latent amount of data from social networking sites in order to model and study human disease spreading and behaviour patterns. Auction houses and marketing companies can leverage on these, findings to boost their online marketing and auctions strategies . Furthermore, the police and other security organizations can leverage on the finding from this research to enhance their know-how on combating terrorism and financial frauds, through the use of these network theory tools.