A new model for calculating the maximum trust in Online Social Networks and solving by Artificial Bee Colony algorithm

Q1 Mathematics

Computational Social Networks Pub Date : 2020-02-13 DOI:10.1186/s40649-020-00077-6

Shahram Saeidi

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引用次数: 9

Abstract

The social networks are widely used by millions of people worldwide. The trust concept is one of the most important issues in Social Network Analysis (SNA) which highly affects the quantity and quality of the inter-connections, decisions, and interactions among the users in e-commerce or recommendation systems. Many normative algorithms are developed to calculate the trust which most of them are complicated, depend on the network structure, and need lots of critical information that makes them hard to use. The aim of this paper is proposing a descriptive, simple and effective method for calculating the maximal trust and the trust route between any two users of an Online Social Network (OSN). For this purpose, four new models for estimating the trust mechanism of the users are proposed and analyzed using Kolmogorov–Smirnov and Anderson–Darling statistical hypothesis tests to identify and validate the best-fitted model based on 20,613 empirical results gathered from 4552 social network volunteers. Due to the time–complexity of the problem, a meta-heuristic algorithm based on the Artificial Bee Colony (ABC) optimization method is also developed for solving the best-fitted model. The proposed algorithm is simulated in Matlab® over six larger test cases adopted from the Facebook dataset. In order to evaluate the performance of the developed algorithm, the Ant Colony Optimization (ACO) and Genetic Algorithm (GA) based meta-heuristics are also simulated on the same test cases. The comparison of the computational results shows that the ABC approach performs better than the ACO and GA as the size of the network increases.

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基于人工蜂群算法的在线社交网络最大信任计算模型

社交网络被全世界数以百万计的人广泛使用。信任概念是社会网络分析(Social Network Analysis, SNA)中最重要的问题之一，它对电子商务或推荐系统中用户之间的联系、决策和交互的数量和质量有着重要的影响。目前已经开发了许多用于计算信任的规范算法，但这些算法大多复杂，依赖于网络结构，并且需要大量的关键信息，这使得它们难以使用。本文的目的是提出一种描述性的、简单有效的方法来计算在线社交网络(Online Social Network, OSN)中任意两个用户之间的最大信任和信任路由。为此，本文提出了估算用户信任机制的四种新模型，并利用Kolmogorov-Smirnov和Anderson-Darling统计假设检验对其进行了分析，以4552名社会网络志愿者的20,613个实证结果为基础，识别并验证了最适合的模型。考虑到问题的时间复杂度，提出了一种基于人工蜂群(Artificial Bee Colony, ABC)优化方法的元启发式算法来求解最优拟合模型。所提出的算法在Matlab®中通过从Facebook数据集采用的六个更大的测试用例进行模拟。为了评估所开发算法的性能，还在相同的测试用例上模拟了基于蚁群优化(ACO)和遗传算法(GA)的元启发式算法。计算结果的比较表明，随着网络规模的增大，ABC算法的性能优于蚁群算法和遗传算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.