16 citations · 17 across the 14 of their papers we have counts for
8 papers · 1 filter
Adapting to Heterophilic Graph Data with Structure-Guided Neighbor Discovery
Victor M. Tenorio, Madeline Navarro, Samuel Rey +2
Graph Neural Networks (GNNs) often struggle with heterophilic data, where connected nodes may have dissimilar labels, as they typically assume homophily and rely on local message p…
A Few Moments Please: Scalable Graphon Learning via Moment Matching
Reza Ramezanpour, Victor M. Tenorio, Antonio G. Marques +2
Graphons, as limit objects of dense graph sequences, play a central role in the statistical analysis of network data. However, existing graphon estimation methods often struggle wi…
Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation
Victor M. Tenorio, Nicolas Zilberstein, Santiago Segarra +1
Diffusion models have emerged as powerful generative models for graph generation, yet their use for conditional graph generation remains a fundamental challenge. In particular, gui…
Structure-Guided Input Graph for GNNs facing Heterophily
Victor M. Tenorio, Madeline Navarro, Samuel Rey +2
Graph Neural Networks (GNNs) have emerged as a promising tool to handle data exhibiting an irregular structure. However, most GNN architectures perform well on homophilic datasets,…
Exploiting the Structure of Two Graphs with Graph Neural Networks
Victor M. Tenorio, Antonio G. Marques
Graph neural networks (GNNs) have emerged as a promising solution to deal with unstructured data, outperforming traditional deep learning architectures. However, most of the curren…
Redesigning graph filter-based GNNs to relax the homophily assumption
Samuel Rey, Madeline Navarro, Victor M. Tenorio +2
Graph neural networks (GNNs) have become a workhorse approach for learning from data defined over irregular domains, typically by implicitly assuming that the data structure is rep…
Robust Graph Neural Network based on Graph Denoising
Victor M. Tenorio, Samuel Rey, Antonio G. Marques
Graph Neural Networks (GNNs) have emerged as a notorious alternative to address learning problems dealing with non-Euclidean datasets. However, although most works assume that the…
Recovering Missing Node Features with Local Structure-based Embeddings
Victor M. Tenorio, Madeline Navarro, Santiago Segarra +1
Node features bolster graph-based learning when exploited jointly with network structure. However, a lack of nodal attributes is prevalent in graph data. We present a framework to…