# Sparse Subsystem Discovery for Intelligent Sensor Networks

## TL;DR

This paper introduces RL-SGF, a new method using reinforcement learning to efficiently find sparse subsystems in intelligent sensor networks.

## Contribution

The novel RL-SGF framework combines deep reinforcement learning and graph embedding for scalable sparse subsystem discovery.

## Key findings

- RL-SGF outperforms existing algorithms in efficiency and solution quality on synthetic and real-world datasets.
- The framework enhances model effectiveness and robustness in sensor network applications through joint optimization.
- Results show applicability to real-world sparse subsystem discovery in intelligent sensor networks.

## Abstract

The Sparse Subgraph Finding (SGF) problem addresses the challenge of identifying sub–graphs with weak social interactions and sparse connections within a graph, which can be effectively modeled as discovering sparse subsystems in intelligent sensor networks. Traditional methods often rely on manually designed heuristics, which are computationally expensive and lack scalability, especially when dealing with complex sensor network systems. In this paper, we propose RL-SGF, a novel framework that integrates deep reinforcement learning and graph embedding through joint optimization to overcome these limitations. By simultaneously optimizing subsystem sparsity and representation learning within a unified framework, RL-SGF enhances both the effectiveness and robustness of the model in sensor network applications. Experimental results on synthetic and real-world datasets, including social networks, citation networks, and sensor network simulations, demonstrate that RL-SGF outperforms existing algorithms in terms of efficiency and solution quality, making it highly applicable to real-world sparse subsystem discovery scenarios in intelligent sensor networks.