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Predictive simulation framework of stochastic diffusion model for identifying top-K influential nodes

Citation

Ohara, K and Saito, K and Kimura, M and Motoda, H, Predictive simulation framework of stochastic diffusion model for identifying top-K influential nodes, Proceedings of the Fifth Asian Conference on Machine Learning 2013, 13-15 November 2013, Canberra, Australia, pp. 149-164. ISSN 1532-4435 (2013) [Refereed Conference Paper]


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Copyright Statement

Copyright 2013 K. Ohara, K. Saito, M. Kimura & H. Motoda

Official URL: http://jmlr.org/proceedings/papers/v29/

Abstract

We address a problem of efficiently estimating the influence of a node in information di ffusion over a social network. Since the information di ffusion is a stochastic process, the influence degree of a node is quantified by the expectation, which is usually obtained by very time consuming many runs of simulation. Our contribution is that we proposed a framework for predictive simulation based on the leave-N-out cross validation technique that well approximates the error from the unknown ground truth for two target problems: one to estimate the influence degree of each node, and the other to identify top-K influential nodes. The method we proposed for the first problem estimates the approximation error of the influence degree of each node, and the method for the second problem estimates the precision of the derived top-K nodes, both without knowing the true influence degree. We experimentally evaluate the proposed methods using the three real world networks, and show that they can serve as a good measure to solve the target problems with far fewer runs of simulation ensuring the accuracy if N is appropriately chosen, and that estimating the top-K nodes is easier than estimating the influence degree, which means one can identify the influential nodes without knowing exactly their influence degree.

Item Details

Item Type:Refereed Conference Paper
Keywords:predictive simulation, influence degree, information diffusion
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Artificial Intelligence and Image Processing not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Computer Software and Services not elsewhere classified
Author:Motoda, H (Dr Hiroshi Motoda)
ID Code:89001
Year Published:2013
Deposited By:Computing and Information Systems
Deposited On:2014-02-22
Last Modified:2017-11-18
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