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The heterophilic graph learning handbook: Benchmarks, models, theoretical analysis, applications and challenges
Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …
be connected, has been commonly believed to be the main reason for the superiority of …
A survey on oversmoothing in graph neural networks
Node features of graph neural networks (GNNs) tend to become more similar with the
increase of the network depth. This effect is known as over-smoothing, which we …
increase of the network depth. This effect is known as over-smoothing, which we …
When do graph neural networks help with node classification? investigating the homophily principle on node distinguishability
Homophily principle, ie, nodes with the same labels are more likely to be connected, has
been believed to be the main reason for the performance superiority of Graph Neural …
been believed to be the main reason for the performance superiority of Graph Neural …
Difformer: Scalable (graph) transformers induced by energy constrained diffusion
Real-world data generation often involves complex inter-dependencies among instances,
violating the IID-data hypothesis of standard learning paradigms and posing a challenge for …
violating the IID-data hypothesis of standard learning paradigms and posing a challenge for …
A hitchhiker's guide to geometric gnns for 3d atomic systems
Recent advances in computational modelling of atomic systems, spanning molecules,
proteins, and materials, represent them as geometric graphs with atoms embedded as …
proteins, and materials, represent them as geometric graphs with atoms embedded as …
A fractional graph laplacian approach to oversmoothing
Graph neural networks (GNNs) have shown state-of-the-art performances in various
applications. However, GNNs often struggle to capture long-range dependencies in graphs …
applications. However, GNNs often struggle to capture long-range dependencies in graphs …
Gread: Graph neural reaction-diffusion networks
Graph neural networks (GNNs) are one of the most popular research topics for deep
learning. GNN methods typically have been designed on top of the graph signal processing …
learning. GNN methods typically have been designed on top of the graph signal processing …
Mixtures recomposition by neural nets: a multidisciplinary overview
Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures,
providing an expressive view of the chemical space and multiscale processes. Their …
providing an expressive view of the chemical space and multiscale processes. Their …
Improving graph neural networks with learnable propagation operators
Abstract Graph Neural Networks (GNNs) are limited in their propagation operators. In many
cases, these operators often contain non-negative elements only and are shared across …
cases, these operators often contain non-negative elements only and are shared across …
Understanding convolution on graphs via energies
Graph Neural Networks (GNNs) typically operate by message-passing, where the state of a
node is updated based on the information received from its neighbours. Most message …
node is updated based on the information received from its neighbours. Most message …