Extra Form
Lecturer 정교민
date Nov 01, 2012
Diffusion of information, rumors or epidemics via various social networks has been extensively studied for decades. In particular, Kempe, Kleinberg, and Tardos (KDD '03) proposed the general threshold model, a generalization of many mathematical models for diffusion on networks which is based on utility maximization of individuals in game theoretic consideration. Despite its importance, the analysis under the threshold model, however, has concentrated on special cases such as the submodular influence (by Mossel-Roch (STOC '07)), homogeneous thresholds (by Whitney(Phys. Rev. E. '10)), and locally tree-like networks (by Watts(PNAS '02)). We first consider the general threshold model with arbitrary threshold distribution on arbitrary networks. We prove that only if (essentially) all nodes have degrees \omega(log n), the final cascade size is highly concentrated around its mean with high probability for a large class of general threshold models including the linear threshold model, and the Katz-Shapiro pricing model. We also prove that in those cases, somewhat surprisingly, the expectation of the cascade size is asymptotically independent of the network structure if initial adopters are chosen by public advertisements, and provide a formula to compute the cascade size. Our formula allows us to compute when a phase transition for a large spreading (a tipping point) happens. We then provide a novel algorithm for influence maximization that integrates a new message passing based influence ranking and influence estimation methods in the independent cascade model.
Attachment '1'
List of Articles
Category Subject Dept. Lecturer
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BK21 FOUR Rookies Pitch 2022-1 Rookies Pitch:Functional Analysis (정민구) file 고등과학원 정민구
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BK21 FOUR Rookies Pitch 2021-1 Rookies Pitch: Financial Mathematics(전재기), PDE, Kinetic Equation(배기찬) file BK21 FOUR 전재기, 배기찬
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BK21 FOUR Rookies Pitch 2021-2 Rookies Pitch: Representation Theory(장일승) file 서울대학교 장일승
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