Graph representation learning in smart environments
This research focuses on learning effective representations of graph-structured data to improve inference in smart environments. While state-of-the-art deep learning models are highly successful on grids or sequences, graphs pose unique challenges due to their arbitrary size, irregular structure, and lack of an explicit notion of locality. These characteristics make it nontrivial to directly apply conventional architectures, such as convolutional neural networks, to graph-based data.
Andres Tello Guerrero investigates Human Activity Recognition (HAR) using wearable and mobile sensors, where the graph topology is not explicitly given and must be inferred from data. In this setting, HAR is framed as a graph classification problem. Tello Guerrero highlights how conventional HAR methodologies can lead to misleading and impractical results, motivating the use of unbiased evaluation strategies. He then proposes a contrastive learning approach to capture global and local dependencies based on multiple graph constructions, leading to significant improvements in classification accuracy.
Tello Guerrero furthermore focuses on Water Distribution Networks (WDNs), where the graph topology is explicitly defined by the network layout. The task is formulated as a node-level regression problem aimed at reconstructing pressure signals across the network from sparsely located sensors. Tello Guerrero introduces a graph-based model, along with robust training and evaluation strategies, resulting in a new state-of-the-art approach. Finally, Tello Guerrero discusses how these advances can support the development of Graph Foundation Models tailored to WDNs, outlining key challenges and strategies for their realization.