Friends-Based Crowdsourcing: Algorithms For Task Dissemination Over Social Groups

Abstract

Abstract 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.