Application of the Ant Colony Optimization Algorithm to Competitive Viral Marketing

Yang, Wan-Shiou; Weng, Shi-Xin

doi:10.1007/978-3-642-30448-4_1

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…GA and DE were used to increase the number of active nodes, while PSO focused on recording the best node position and speed within the population. In the chain of evolutionary computation and swarm algorithms, the ant colony optimization (ACO) algorithm as a swarm intelligence method has been applied to the competitive maximization problem [59]. ACO aims to select a set of nodes and links that maximizes influence by increasing the spread within the network.…”

Section: ) Evolutionary Methodsmentioning

confidence: 99%

Toward Information Diffusion Model for Viral Marketing in Business

AlSuwaidan¹,

Ykhlef²

2016

ijacsa

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Abstract-Current obstacles in the study of social media marketing include dealing with massive data and real-time updates have motivated to contribute solutions that can be adopted for viral marketing. Since information diffusion and social networks are the core of viral marketing, this article aims to investigate the constellation of diffusion methods for viral marketing. Studies on diffusion methods for viral marketing have applied different computational methods, but a systematic investigation of these methods has limited. Most of the literature have focused on achieving objectives such as influence maximization or community detection. Therefore, this article aims to conduct an in-depth review of works related to diffusion for viral marketing. Viral marketing has applied to business-to-consumer transactions but has seen limited adoption in business-to-business transactions. The literature review reveals a lack of new diffusion methods, especially in dynamic and large-scale networks. It also offers insights into applying various mining methods for viral marketing. It discusses some of the challenges, limitations, and future research directions of information diffusion for viral marketing. The article also introduces a viral marketing information diffusion model. The proposed model attempts to solve the dynamicity and large-scale data of social networks by adopting incremental clustering and a stochastic differential equation for business-to-business transactions.

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Section: ) Evolutionary Methodsmentioning

confidence: 99%

Toward Information Diffusion Model for Viral Marketing in Business

AlSuwaidan¹,

Ykhlef²

2016

ijacsa

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“…Analyzing previous studies we can conclude that more complex metaheuristic approaches usually result in better solutions than simple greedy approaches. Yang and Weng (2012) proposed an Ant Colony Optimization (ACO) algorithm based on a parameterized probabilistic model to address the SNIMP. They used the degree centrality, distance centrality, and simulated influence methods for determining the heuristic values.…”

Section: Literature Reviewmentioning

confidence: 99%

A quick GRASP-based method for influence maximization in social networks

Lozano-Osorio

Sánchez‐Oro

Duarte

et al. 2021

J Ambient Intell Human Comput

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The evolution and spread of social networks have attracted the interest of the scientific community in the last few years. Specifically, several new interesting problems, which are hard to solve, have arisen in the context of viral marketing, disease analysis, and influence analysis, among others. Companies and researchers try to find the elements that maximize profit, stop pandemics, etc. This family of problems is collected under the term Social Network Influence Maximization problem (SNIMP), whose goal is to find the most influential users (commonly known as seeds) in a social network, simulating an influence diffusion model. SNIMP is known to be an $$\mathcal {NP}$$ NP -hard problem and, therefore, an exact algorithm is not suitable for solving it optimally in reasonable computing time. The main drawback of this optimization problem lies on the computational effort required to evaluate a solution. Since each node is infected with a certain probability, the objective function value must be calculated through a Monte Carlo simulation, resulting in a computationally complex process. The current proposal tries to overcome this limitation by considering a metaheuristic algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP) framework to design a quick solution procedure for the SNIMP. Our method consists of two distinct stages: construction and local search. The former is based on static features of the network, which notably increases its efficiency since it does not require to perform any simulation during construction. The latter involves a local search based on an intelligent neighborhood exploration strategy to find the most influential users based on swap moves, also aiming for an efficient processing. Experiments performed on 7 well-known social network datasets with 5 different seed set sizes confirm that the proposed algorithm is able to provide competitive results in terms of quality and computing time when comparing it with the best algorithms found in the state of the art.

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Application of the Ant Colony Optimization Algorithm to Competitive Viral Marketing

Cited by 2 publications

References 13 publications

Toward Information Diffusion Model for Viral Marketing in Business

Toward Information Diffusion Model for Viral Marketing in Business

A quick GRASP-based method for influence maximization in social networks

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