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Found 13 papers, 2 papers with code
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets.
Effective Protein-Protein Interaction Exploration with PPIretrieval
Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, and immune defense.
MUDiff: Unified Diffusion for Complete Molecule Generation
Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.
When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability
Homophily principle, i. e., 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 Networks (GNNs) over Neural Networks on node classification tasks.
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood information of nodes.
When Do We Need Graph Neural Networks for Node Classification?
Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by additionally making use of graph structure based on the relational inductive bias (edge bias), rather than treating the nodes as collections of independent and identically distributed (i. i. d.)
Revisiting Heterophily For Graph Neural Networks
ACM is more powerful than the commonly used uni-channel framework for node classification tasks on heterophilic graphs and is easy to be implemented in baseline GNN layers.
Inductive Bias
Node Classification on Non-Homophilic (Heterophilic) Graphs
Graph Neural Networks Intersect Probabilistic Graphical Models: A Survey
Graph Neural Networks (GNNs) are new inference methods developed in recent years and are attracting growing attention due to their effectiveness and flexibility in solving inference and learning problems over graph-structured data.
High-Order Pooling for Graph Neural Networks with Tensor Decomposition
Graph Neural Networks (GNNs) are attracting growing attention due to their effectiveness and flexibility in modeling a variety of graph-structured data.
Is Heterophily A Real Nightmare For Graph Neural Networks on Performing Node Classification?
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
Ranked #1 on
Node Classification
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Is Heterophily A Real Nightmare For Graph Neural Networks Performing Node Classification?
In this paper, we first show that not all cases of heterophily are harmful for GNNs with aggregation operation.
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filters the neighborhood node information.
