HGNN+: General Hypergraph Neural Networks
Graph Neural Networks have attracted increasing attention in recent years. However,
existing GNN frameworks are deployed based upon simple graphs, which limits their …
existing GNN frameworks are deployed based upon simple graphs, which limits their …
Heterogeneous hypergraph variational autoencoder for link prediction
Link prediction aims at inferring missing links or predicting future ones based on the
currently observed network. This topic is important for many applications such as social …
currently observed network. This topic is important for many applications such as social …
Hypergraph transformer for skeleton-based action recognition
Skeleton-based action recognition aims to recognize human actions given human joint
coordinates with skeletal interconnections. By defining a graph with joints as vertices and …
coordinates with skeletal interconnections. By defining a graph with joints as vertices and …
Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks
The natural world is full of complex systems characterized by intricate relations between
their components: from social interactions between individuals in a social network to …
their components: from social interactions between individuals in a social network to …
Equivariant hypergraph diffusion neural operators
Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide
a promising way to model higher-order relations in data and further solve relevant prediction …
a promising way to model higher-order relations in data and further solve relevant prediction …
Hypersage: Generalizing inductive representation learning on hypergraphs
Graphs are the most ubiquitous form of structured data representation used in machine
learning. They model, however, only pairwise relations between nodes and are not …
learning. They model, however, only pairwise relations between nodes and are not …
Pina: Leveraging side information in extreme multi-label classification via predicted instance neighborhood aggregation
The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an
exceptionally large label space. Most of the existing XMC learners focus on the extraction of …
exceptionally large label space. Most of the existing XMC learners focus on the extraction of …
Line hypergraph convolution network: Applying graph convolution for hypergraphs
Network representation learning and node classification in graphs got significant attention
due to the invent of different types graph neural networks. Graph convolution network (GCN) …
due to the invent of different types graph neural networks. Graph convolution network (GCN) …
Hypergraph attention isomorphism network by learning line graph expansion
S Bandyopadhyay, K Das… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Graph neural networks (GNNs) are able to achieve state-of-the-art performance for node
representation and classification in a network. But, most of the existing GNNs can be applied …
representation and classification in a network. But, most of the existing GNNs can be applied …
Active clustering for labeling training data
Gathering training data is a key step of any supervised learning task, and it is both critical
and expensive. Critical, because the quantity and quality of the training data has a high …
and expensive. Critical, because the quantity and quality of the training data has a high …