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Friends-Based Crowdsourcing: Algorithms For Task Dissemination Over Social Groups
The Computer Journal ( IF 1.4 ) Pub Date : 2021-07-18 , DOI: 10.1093/comjnl/bxab093
Zhiyao Li 1 , Wei Liu 1 , Xiaofeng Gao 1 , Guihai Chen 1
Affiliation  

Crowdsourcing has become increasingly popular in recent years. In order to achieve the optimal task allocation, one of the most important issues is to select more suitable crowdworkers. By leveraging its pervasiveness, social network can be employed as a novel worker recruitment platform. A robust task allocation scheme over the social network could also consider the word-of-mouth (WoM) mode, in which tasks are delivered from workers to workers. In this paper, we discuss an Non-deterministic Polynomial-Hard (NP-Hard) problem, cost-effective and budget-balanced task allocation (CBTA) problem under the WoM mode in social groups. We propose two heuristic algorithms: CB-greedy and CB-local based on greedy strategy and local search technique, respectively. We also prove that the running time of CB-greedy is $O(m^2\log m)$, whereas CB-local utilizing disjoint-set achieves $O(mn\alpha (m, n))$, where $m$ is the number of edges indicating interactions of social groups, $n$ is the number of social groups and $\alpha $ is the inverse Ackerman function. Extensive experiments validate the efficiency and performance of our proposed algorithms.

中文翻译:

基于朋友的众包:社会群体任务传播的算法

近年来,众包变得越来越流行。为了实现最优的任务分配,最重要的问题之一是选择更合适的众包。通过利用其普遍性,社交网络可以用作一种新颖的工人招聘平台。社交网络上的强大任务分配方案还可以考虑口碑(WoM)模式,其中任务从工人交付给工人。在本文中,我们讨论了社会群体中 WoM 模式下的非确定性多项式硬 (NP-Hard) 问题、成本效益和预算平衡任务分配 (CBTA) 问题。我们提出了两种启发式算法:分别基于贪心策略和局部搜索技术的 CB-greedy 和 CB-local。我们还证明了CB-greedy的运行时间为$O(m^2\log m)$,而 CB-local 利用不相交集实现 $O(mn\alpha (m, n))$,其中 $m$ 是表示社会群体交互的边数,$n$ 是社会群体的数量,$\ alpha $ 是反阿克曼函数。大量实验验证了我们提出的算法的效率和性能。
更新日期:2021-07-18
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