# Service Discovery in Social Internet of Things using Graph Neural Networks

arXiv: https://arxiv.org/abs/2205.12711 · HTML: https://mlsanity.com/paper/2205.12711/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 25 May 2022.

## Builds on (most cited first, 5 of 5)

- [Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks](https://mlsanity.com/paper/2106.00717/) (2021)
- [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)
- [Towards Collaborative Mobile Crowdsourcing](https://mlsanity.com/paper/2104.05626/) (2021)
- [Automated Service Discovery for Social Internet-of-Things Systems](https://mlsanity.com/paper/2003.11524/) (2020)

## Built on by (most cited first, 2 of 2)

- [GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers](https://mlsanity.com/paper/2210.17281/) (2022)
- [Resource Allocation in Hybrid Radio-Optical IoT Networks using GNN with Multi-task Learning](https://mlsanity.com/paper/2511.07428/) (2025)

## Abstract (arXiv)

Internet-of-Things (IoT) networks intelligently connect thousands of physical entities to provide various services for the community. It is witnessing an exponential expansion, which is complicating the process of discovering IoT devices existing in the network and requesting corresponding services from them. As the highly dynamic nature of the IoT environment hinders the use of traditional solutions of service discovery, we aim, in this paper, to address this issue by proposing a scalable resource allocation neural model adequate for heterogeneous large-scale IoT networks. We devise a Graph Neural Network (GNN) approach that utilizes the social relationships formed between the devices in the IoT network to reduce the search space of any entity lookup and acquire a service from another device in the network. This proposed resource allocation approach surpasses standardization issues and embeds the structure and characteristics of the social IoT graph, by the means of GNNs, for eventual clustering analysis process. Simulation results applied on a real-world dataset illustrate the performance of this solution and its significant efficiency to operate on large-scale IoT networks.