The Structure and Dynamics of Information Sharing Networks.

Information Sharing Networks;Information Diffusion;Network Structures;Computer Science;Engineering;Computer Science & Engineering

Shi, XiaolinRadev, Dragomir Radkov ;

University of Michigan

关键词: Information Sharing Networks;    Information Diffusion;    Network Structures;    Computer Science;    Engineering;    Computer Science & Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/63681/shixl_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Information flows are produced, carried, and directed by information sharing networks. The evolution of the structure of such networks and the way information diffuses are affected by one another. This thesis studies structural features of information networks and their relationships with information diffusion. It starts with the robustness study of topological features when the datasets are sampled from networks that are rapidly evolving, as is the case in large-scale online blog networks. The features of blog networks are found to be stable upon aggregation with comprehensive data sets, even as individual network ties are highly intermittent. Another salient structural feature of such networks is that a small number of vertices play a disproportionately important role through their position and connectivity. In several online information networks studied in this thesis, one can construct subgraphs of vertices according to different importance measures, while consistently preserving their attributes such as connectivity and shortest paths in the original networks. In this thesis, we further show that a special set of edges, strong ties, percolate through online social networks. Networks of strong ties do not break up into isolated communities, nor are their average shortest paths lengthened significantly.After examining the structural features that will potentially affect the flow of information, this thesis further examines the actual relationship between structure and information diffusion. A thorough analysis based on citation networks indicates that information is less likely to flow across community boundaries, and a publication;;s citing across disciplines is tied to its subsequent impact. In the case of patents and natural science publications, those that are cited at least once are cited slightly more when they draw on research outside of their area. This thesis also studies information diffusion in online communities. We investigate the information diffusion curves that reveal the patterns of user behavior in joining groups, and analyze the feature factors associated with users or groups that influence such behavior. Bipartite Markov Random Field (BiMRF) models are built to help understand the relationships of these features, as well as the differences in their impact in different types of online forums.