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2022 | OriginalPaper | Buchkapitel

Mixed Integer Programming and LP Rounding for Opinion Maximization on Directed Acyclic Graphs

verfasst von : Po-An Chen, Ya-Wen Cheng, Yao-Wei Tseng

Erschienen in: Complex Networks & Their Applications X

Verlag: Springer International Publishing

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Abstract

Gionis et al. have already proposed a greedy algorithm and some heuristics for the opinion maximization problem. Unlike their approach, we adopt mathematical programming to solve the opinion maximization problem on specific classes of networks. We find that on directed acyclic graphs, opinion influence between nodes will not cycle, but would spread outwards from influencers. Based on such an insight, we model the problem as a mixed integer programming (MIP) problem and relax the MIP to a linear program (LP). With MIP, we obtain optimal solutions for the opinion maximization problem and derive approximation solutions with LP randomized rounding algorithms. We conduct experiments for one LP randomized rounding algorithm and give an analysis of the approximation ratio for the other LP randomized rounding algorithm.

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Fußnoten
1
Also, although our main results are motivated from and established for directed acyclic graphs, what if a given graph is not acyclic but is very close to an acyclic graph? Our conjecture is that the approach can be extended for such a graph since we have not yet observed anything that would immediately prevent us from obtaining an approximation solution, but the feasibility in a probabilistic sense and approximation ratio guarantees may change a lot.
 
2
The uniqueness follows from the convexity of cost functions.
 
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Metadaten
Titel
Mixed Integer Programming and LP Rounding for Opinion Maximization on Directed Acyclic Graphs
verfasst von
Po-An Chen
Ya-Wen Cheng
Yao-Wei Tseng
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-030-93409-5_71

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