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Found 11 papers, 5 papers with code
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks
Graph convolutional networks (GCNs) have shown promising results in processing graph data by extracting structure-aware features.
Taxonomy of Benchmarks in Graph Representation Learning
Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry.
Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?
We propose a geometric scattering-based graph neural network (GNN) for approximating solutions of the NP-hard maximum clique (MC) problem.
Geometric Scattering Attention Networks
Geometric scattering has recently gained recognition in graph representation learning, and recent work has shown that integrating scattering features in graph convolution networks (GCNs) can alleviate the typical oversmoothing of features in node representation learning.
Data-Driven Learning of Geometric Scattering Networks
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters.
Towards a Taxonomy of Graph Learning Datasets
Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data.
Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks
We further introduce an attention framework that allows the model to locally attend over combined information from different filters at the node level.
Learnable Filters for Geometric Scattering Modules
We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters.
Inferring dynamic regulatory interaction graphs from time series data with perturbations
Complex systems are characterized by intricate interactions between entities that evolve dynamically over time.
On the Scalability of GNNs for Molecular Graphs
However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to the lower efficiency of sparse operations, large data requirements, and lack of clarity about the effectiveness of various architectures.
