Hyperparametric Robust and Dynamic Influence Maximization

We study the problem of robust influence maximization in dynamic diffusion networks. In line with recent works, we consider the scenario where the network can undergo insertion and removal of nodes and edges, in discrete time steps, and the influence weights are determined by the features of the corresponding nodes and a global hyperparameter. Given this, our goal is to find, at every time step, the seed set maximizing the worst-case influence spread across all possible values of the hyperparameter. We propose an approximate solution using multiplicative weight updates and a greedy algorithm, with theoretical quality guarantees. Our experiments validate the effectiveness and efficiency of the proposed methods.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i12.33362
Primary Topic
Statistical Methods and Inference
Type
article
Field-Weighted Citation Impact
0.00

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article

Hyperparametric Robust and Dynamic Influence Maximization

Xiaokui Xiao, Arkaprava Saha, Laks V. S. Lakshmanan, Bogdan Cautis
Proceedings of the AAAI Conference on Artificial Intelligence
Statistical Methods and Inference
article

Hyperparametric Robust and Dynamic Influence Maximization

Xiaokui Xiao, Arkaprava Saha, Laks V. S. Lakshmanan, Bogdan Cautis
article en

Abstract

We study the problem of robust influence maximization in dynamic diffusion networks. In line with recent works, we consider the scenario where the network can undergo insertion and removal of nodes and edges, in discrete time steps, and the influence weights are determined by the features of the corresponding nodes and a global hyperparameter. Given this, our goal is to find, at every time step, the seed set maximizing the worst-case influence spread across all possible values of the hyperparameter. We propose an approximate solution using multiplicative weight updates and a greedy algorithm, with theoretical quality guarantees. Our experiments validate the effectiveness and efficiency of the proposed methods.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(12)
Centre National de la Recherche Scientifique (FR), University of British Columbia (CA), National University of Singapore (SG), Université Paris-Saclay (FR), Center for Research and Interdisciplinarity (FR), Laboratoire d'Informatique de Grenoble (FR), Université Grenoble Alpes (FR)
National Research Foundation, National Supercomputing Centre Singapore, Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 77%
Statistical Methods and Inference
DOIOpen Access PDFOpenAlex
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Hyperparametric Robust and Dynamic Influence Maximization — Xiaokui Xiao, Arkaprava Saha, et al. · Proceedings of the AAAI Conference on Artificial Intelligence (2025) | TGRS Research Map | TGRS