A Weighted Artificial Bee Colony algorithm for influence maximization

Cantini, Riccardo; Marozzo, Fabrizio; Mazza, Silvio; Talia, Domenico; Trunfio, Paolo

doi:10.1016/j.osnem.2021.100167

Cited by 10 publications

(2 citation statements)

References 19 publications

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“…The main goal of the system is to maximize the nectar collection and it can be adapted to the influence maximization problem as described in [64,65]. Specifically, each user of a social network is considered as a source of food, employer bees identify the opinion leaders of the network (i.e., final seeds), scout bees are used for exploring the neighborhood of employer bees, and on-looker bees indicate the influenced users.…”

Section: Programming Examplementioning

confidence: 99%

Programming big data analysis: principles and solutions

et al. 2022

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In the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. This data, commonly referred to as Big Data, is challenging current storage, processing, and analysis capabilities. New models, languages, systems and algorithms continue to be developed to effectively collect, store, analyze and learn from Big Data. Most of the recent surveys provide a global analysis of the tools that are used in the main phases of Big Data management (generation, acquisition, storage, querying and visualization of data). Differently, this work analyzes and reviews parallel and distributed paradigms, languages and systems used today to analyze and learn from Big Data on scalable computers. In particular, we provide an in-depth analysis of the properties of the main parallel programming paradigms (MapReduce, workflow, BSP, message passing, and SQL-like) and, through programming examples, we describe the most used systems for Big Data analysis (e.g., Hadoop, Spark, and Storm). Furthermore, we discuss and compare the different systems by highlighting the main features of each of them, their diffusion (community of developers and users) and the main advantages and disadvantages of using them to implement Big Data analysis applications. The final goal of this work is to help designers and developers in identifying and selecting the best/appropriate programming solution based on their skills, hardware availability, application domains and purposes, and also considering the support provided by the developer community.

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Section: Programming Examplementioning

confidence: 99%

Programming big data analysis: principles and solutions

et al. 2022

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“…However, such approaches usually obtain more efficiently solutions than greedy algorithms by sacrificing large accuracy, which is inapplicable to practical scenarios. Meta-heuristic algorithms (Zareie et al 2020;Cantini et al 2021;Arora and Singh 2019) that simulate the foraging behavior of biological populations or the transformation of physical phenomenon features were recently utilized for influence maximization. Such methods can avoid the time-consuming Monte Carlo simulations so that the solution efficiency is accelerated compared to greedy algorithms, and the solution accuracy is improved significantly compared to centrality-based heuristics through discrete evolutionary mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Maximizing the influence spread in social networks: a learning automata driven discrete butterfly optimization algorithm

Tang

Zhu

Lan

et al. 2023

Preprint

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Influence maximization aims at the identification of a small group of individuals that could result in the most widely transmission of information in social networks. Although greedy-based algorithms can yield reliable solutions, the computational cost is extreme expensive especially in large-scale networks. Meanwhile, the traditional heuristics such as degree and betweenness centralities tend to suffer from the problem of low accuracy. Locating influential nodes effectively using swarm intelligence algorithms remains a research hotspot in social network analysis. To solve the influence maximization problem in an efficient way, a learning automata driven discrete butterfly optimization algorithm(LA-DBOA) mapped into the network topology is proposed in this paper. According to the LA-DBOA framework, a novel encoding mechanism and discrete evolution rules adapted to network topology are presented. A modified learning automata is adopted to guide the butterfly population towards promising areas. Based on the topological characteristics of the discrete network, a new local search strategy is conceived to enhance the search performance of the butterflies. Extensive experiments are conducted on six real networks under the independent cascade model, and the results demonstrate that the proposed algorithm outperforms the state-of-the-art methods when trade-off is made between efficiency and effectiveness.

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Topic Detection and Tracking in Social Media Platforms

Cantini

Marozzo

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A Weighted Artificial Bee Colony algorithm for influence maximization

Cited by 10 publications

References 19 publications

Programming big data analysis: principles and solutions

Programming big data analysis: principles and solutions

Maximizing the influence spread in social networks: a learning automata driven discrete butterfly optimization algorithm

Topic Detection and Tracking in Social Media Platforms

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