2014 IEEE International Conference on Big Data and Cloud Computing (BdCloud) (2014)
Dec. 3, 2014 to Dec. 5, 2014
Minkyoung Kim , Res. Sch. of Comput. Sci., Australian Nat. Univ., Canberra, ACT, Australia
Peter Christen , Res. Sch. of Comput. Sci., Australian Nat. Univ., Canberra, ACT, Australia
Information spreads across heterogeneous social systems, and the underlying network structures are hard to collect or define. The goal of this paper is to estimate macro-level information diffusion using time-series activity sequences of heterogeneous populations without the need to know detailed network structures. We propose a consistent way of understanding dynamic influence among populations with both model-driven and model-free approaches. As a real-word example, we focus on computer science publications for uncovering research topic diffusion patterns across different sub domains. As a result, estimated diffusion patterns, obtained from the two approaches, exhibit similar information pathways but with different perspectives on diffusion, which in conjunction can help to obtain a more coherent overall picture of diffusion dynamics than either approach alone. We expect that our proposed approaches can help quantify and understand macro-level diffusion across target regions in various real-world scenarios and provide ways of inferring diffusion patterns from time-series real data.
Computational modeling, Computer science, Sociology, Statistics, Data models, Artificial intelligence, Couplings
Minkyoung Kim, D. Newth and P. Christen, "Uncovering Diffusion in Academic Publications Using Model-Driven and Model-Free Approaches," 2014 IEEE International Conference on Big Data and Cloud Computing (BdCloud)(BDCLOUD), Sydney, Australia, 2015, pp. 564-571.