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Found 10 papers, 4 papers with code
Knowledge Base Question Answering through Recursive Hypergraphs
Answering questions typically requires reasoning over multiple links in the given KB.
Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs
Message passing neural network (MPNN) has recently emerged as a successful framework by achieving state-of-the-art performances on many graph-based learning tasks.
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.
Biologically Plausible Neural Networks via Evolutionary Dynamics and Dopaminergic Plasticity
Artificial neural networks (ANNs) lack in biological plausibility, chiefly because backpropagation requires a variant of plasticity (precise changes of the synaptic weights informed by neural events that occur downstream in the neural circuit) that is profoundly incompatible with the current understanding of the animal brain.
Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs
In this paper, we explore GNNs for graph-based SSL of histograms.
KVQA: Knowledge-Aware Visual Question Answering
In spite of this progress, the important problem of answering questions requiring world knowledge about named entities (e. g., Barack Obama, White House, United Nations) in the image has not been addressed in prior research.
Link Prediction in Hypergraphs using Graph Convolutional Networks
Additionally, there is need to represent the direction from reactants to products.
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
Some of the major challenges involved in developing such models are, (i) limited availability of materials data as compared to other fields, (ii) lack of universal descriptor of materials to predict its various properties.
Ranked #4 on
Band Gap
on Materials Project
HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.
Lovasz Convolutional Networks
We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs.
