Paper Info
Title
Uncovering Diffusion in Academic Publications Using Model-Driven and Model-Free Approaches
Abstract
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.
Year
DOI
Venue
2014
10.1109/BDCloud.2014.107
BDCloud
Keywords
Field
DocType
dynamic influence,macro-level diffusion,research topic diffusion patterns,model-driven approach,heterogeneous social systems,model-free approach,time-series real data,heterogeneous populations,academic publications,heterogeneous social networks,computer science education,social networking (online),computer science publications,time series,data models,statistics,computer science,couplings,artificial intelligence,sociology,computational modeling
Data science,Data mining,Data modeling,Computer science,Need to know,Artificial intelligence,Social system,Machine learning,Network structure
Conference
Citations 
PageRank 
References 
2
0.42
9
Authors
3
Name
Order
Citations
PageRank
Minkyoung Kim18511.01
David Newth27722.00
Peter Christen31697107.21