Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategies
This addresses the challenge of efficient decentralized search in social networks, which is incremental by building on prior RL methods to handle privacy and scalability.
The paper tackled the problem of graph path search in large-scale, dynamic, and privacy-sensitive settings by proposing a multi-agent reinforcement learning approach with limited local views, and it demonstrated significant outperformance over learned and heuristic baselines on synthetic and real-world social networks.
Graph path search is a classic computer science problem that has been recently approached with Reinforcement Learning (RL) due to its potential to outperform prior methods. Existing RL techniques typically assume a global view of the network, which is not suitable for large-scale, dynamic, and privacy-sensitive settings. An area of particular interest is search in social networks due to its numerous applications. Inspired by seminal work in experimental sociology, which showed that decentralized yet efficient search is possible in social networks, we frame the problem as a collaborative task between multiple agents equipped with a limited local view of the network. We propose a multi-agent approach for graph path search that successfully leverages both homophily and structural heterogeneity. Our experiments, carried out over synthetic and real-world social networks, demonstrate that our model significantly outperforms learned and heuristic baselines. Furthermore, our results show that meaningful embeddings for graph navigation can be constructed using reward-driven learning.