Integer Linear Programming for Influence Maximization

Baghbani, Farzaneh Ghayour; Asadpour, Masoud; Faili, Heshaam

doi:10.1007/s40998-019-00178-7

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…The linear program's output is seeded using a randomized rounding approach. Some methods solve the IM problem by LP in the LT model, but either their runtime is higher than the greedy algorithm or they tend to produce worse solutions (the spread of the produced seed set is less than the greedy algorithm [28][29][30][31]).…”

Section: Related Literaturementioning

confidence: 99%

A New Algorithm Framework for the Influence Maximization Problem Using Graph Clustering

Agra,

Samuco

2024

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Given a social network modelled by a graph, the goal of the influence maximization problem is to find k vertices that maximize the number of active vertices through a process of diffusion. For this diffusion, the linear threshold model is considered. A new algorithm, called ClusterGreedy, is proposed to solve the influence maximization problem. The ClusterGreedy algorithm creates a partition of the original set of nodes into small subsets (the clusters), applies the SimpleGreedy algorithm to the subgraphs induced by each subset of nodes, and obtains the seed set from a combination of the seed set of each cluster by solving an integer linear program. This algorithm is further improved by exploring the submodularity property of the diffusion function. Experimental results show that the ClusterGreedy algorithm provides, on average, higher influence spread and lower running times than the SimpleGreedy algorithm on Watts–Strogatz random graphs.

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Section: Related Literaturementioning

confidence: 99%

A New Algorithm Framework for the Influence Maximization Problem Using Graph Clustering

Agra,

Samuco

2024

Information

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

Molaei,

Rahsepar Fard,

Bouyer

2024

Neural Comput & Applic

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Modeling the spread of infectious diseases through influence maximization

Yao

Fan

2022

Optim Lett

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Mathematical approaches, such as compartmental models and agent-based models, have been utilized for modeling the spread of the infectious diseases in the computational epidemiology. However, the role of social network structure for transmission of diseases is not explicitly considered in these models. In this paper, the influence maximization problem, considering the diseases starting at some initial nodes with the potential to maximize the spreading in a social network, is adapted to model the spreading process. This approach includes the analysis of network structure and the modeling of connections among individuals with probabilities to be infected. Additionally, individual behaviors that change along the time and eventually influence the spreading process are also included. These considerations are formulated by integer optimization models. Simulation results, based on the randomly generated networks and a local community network under the COVID-19, are performed to validate the effectiveness of the proposed models, and their relationships to the classic compartmental models.

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Integer Linear Programming for Influence Maximization

Cited by 6 publications

References 4 publications

A New Algorithm Framework for the Influence Maximization Problem Using Graph Clustering

A New Algorithm Framework for the Influence Maximization Problem Using Graph Clustering

A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

Modeling the spread of infectious diseases through influence maximization

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