University of Tasmania
Browse

File(s) under permanent embargo

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

conference contribution
posted on 2023-05-23, 08:22 authored by Ohara, K, Saito, K, Kimura, M, Motoda, H
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.

History

Publication title

Proceedings of the Fifth Asian Conference on Machine Learning 2013

Volume

29

Editors

CS Ong and TB Ho

Pagination

149-164

ISSN

1532-4435

Department/School

School of Information and Communication Technology

Publisher

Microtome Publishing

Place of publication

Brookline, MA USA

Event title

Fifth Asian Conference on Machine Learning 2013

Event Venue

Canberra, Australia

Date of Event (Start Date)

2013-11-13

Date of Event (End Date)

2013-11-15

Rights statement

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

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified

Usage metrics

    University Of Tasmania

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC