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Found 7 papers, 5 papers with code
Topological Neural Networks: Mitigating the Bottlenecks of Graph Neural Networks via Higher-Order Interactions
This work starts with a theoretical framework to reveal the impact of network's width, depth, and graph topology on the over-squashing phenomena in message-passing neural networks.
Generalized Simplicial Attention Neural Networks
The aim of this work is to introduce Generalized Simplicial Attention Neural Networks (GSANs), i. e., novel neural architectures designed to process data defined on simplicial complexes using masked self-attentional layers.
Neural Embeddings for Protein Graphs
Proteins perform much of the work in living organisms, and consequently the development of efficient computational methods for protein representation is essential for advancing large-scale biological research.
CIN++: Enhancing Topological Message Passing
Our message passing scheme accounts for the aforementioned limitations by letting the cells to receive also lower messages within each layer.
Ranked #1 on
Graph Classification
on HIV dataset
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Our analysis provides a unified framework to study different recent methods introduced to cope with over-squashing and serves as a justification for a class of methods that fall under graph rewiring.
MaRF: Representing Mars as Neural Radiance Fields
The aim of this work is to introduce MaRF, a novel framework able to synthesize the Martian environment using several collections of images from rover cameras.
Cell Attention Networks
In this paper, we introduce Cell Attention Networks (CANs), a neural architecture operating on data defined over the vertices of a graph, representing the graph as the 1-skeleton of a cell complex introduced to capture higher order interactions.
Ranked #7 on
Graph Classification
on NCI109
