# Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks

arXiv: https://arxiv.org/abs/2106.00717 · HTML: https://mlsanity.com/paper/2106.00717/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 66 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 24 of 24)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [GEMSEC: Graph Embedding with Self Clustering](https://mlsanity.com/paper/1802.03997/) (2018)
- [Attributed Social Network Embedding](https://mlsanity.com/paper/1705.04969/) (2017)
- [A Crowdsourcing Framework for On-Device Federated Learning](https://mlsanity.com/paper/1911.01046/) (2019)
- [CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning](https://mlsanity.com/paper/2009.01674/) (2020)
- [Graph Neural Networks-based Clustering for Social Internet of Things](https://mlsanity.com/paper/2007.03892/) (2020)
- [flexgrid2vec: Learning Efficient Visual Representations Vectors](https://mlsanity.com/paper/2007.15444/) (2020)
- [Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing](https://mlsanity.com/paper/1805.08525/) (2018)
- [An Efficient and Truthful Pricing Mechanism for Team Formation in Crowdsourcing Markets](https://mlsanity.com/paper/1812.04865/) (2018)
- [Optimal Team Recruitment Strategies for Collaborative Mobile Crowdsourcing Systems](https://mlsanity.com/paper/2004.11512/) (2020)
- [A Photo-Based Mobile Crowdsourcing Framework for Event Reporting](https://mlsanity.com/paper/2004.13251/) (2020)
- [BayGo: Joint Bayesian Learning and Information-Aware Graph Optimization](https://mlsanity.com/paper/2011.04345/) (2020)
- [Towards Collaborative Mobile Crowdsourcing](https://mlsanity.com/paper/2104.05626/) (2021)
- [ActiveCrowd: A Framework for Optimized Multi-Task Allocation in Mobile Crowdsensing Systems](https://mlsanity.com/paper/1608.02661/) (2016)
- [Adapting The Secretary Hiring Problem for Optimal Hot-Cold Tier Placement under Top-K Workloads](https://mlsanity.com/paper/1901.07335/) (2019)
- [A Stochastic Team Formation Approach for Collaborative Mobile Crowdsourcing](https://mlsanity.com/paper/2004.13881/) (2020)

## Built on by (most cited first, 4 of 4)

- [Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach](https://mlsanity.com/paper/2306.04366/) (2023)
- [Service Discovery in Social Internet of Things using Graph Neural Networks](https://mlsanity.com/paper/2205.12711/) (2022)
- [A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd](https://mlsanity.com/paper/2301.08563/) (2023)
- [Resource Allocation in Hybrid Radio-Optical IoT Networks using GNN with Multi-task Learning](https://mlsanity.com/paper/2511.07428/) (2025)

## Abstract (arXiv)

Collaborative Mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this paper, we investigate two different CMCS recruitment strategies allowing task requesters to form teams of socially connected and skilled workers: i) a platform-based strategy where the platform exploits its own knowledge about the workers to form a team and ii) a leader-based strategy where the platform designates a group leader that recruits its own suitable team given its own knowledge about its Social Network (SN) neighbors. We first formulate the recruitment as an Integer Linear Program (ILP) that optimally forms teams according to four fuzzy-logic-based criteria: level of expertise, social relationship strength, recruitment cost, and recruiter's confidence level. To cope with NP-hardness, we design a novel low-complexity CMCS recruitment approach relying on Graph Neural Networks (GNNs), specifically graph embedding and clustering techniques, to shrink the workers' search space and afterwards, exploiting a meta-heuristic genetic algorithm to select appropriate workers. Simulation results applied on a real-world dataset illustrate the performance of both proposed CMCS recruitment approaches. It is shown that our proposed low-complexity GNN-based recruitment algorithm achieves close performances to those of the baseline ILP with significant computational time saving and ability to operate on large-scale mobile crowdsourcing platforms. It is also shown that compared to the leader-based strategy, the platform-based strategy recruits a more skilled team but with lower SN relationships and higher cost.