Recipe for a general, powerful, scalable graph transformer
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer
with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph …
with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph …
How powerful are spectral graph neural networks
Abstract Spectral Graph Neural Network is a kind of Graph Neural Network (GNN) based on
graph signal filters. Some models able to learn arbitrary spectral filters have emerged …
graph signal filters. Some models able to learn arbitrary spectral filters have emerged …
Neural bellman-ford networks: A general graph neural network framework for link prediction
Link prediction is a very fundamental task on graphs. Inspired by traditional path-based
methods, in this paper we propose a general and flexible representation learning framework …
methods, in this paper we propose a general and flexible representation learning framework …
Nested graph neural networks
Graph neural network (GNN)'s success in graph classification is closely related to the
Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features …
Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features …
[HTML][HTML] Artificial neural networks in supply chain management, a review
Abstract Artificial Neural Networks (ANNs) are a type of machine learning algorithm inspired
by the structure and function of the human brain. In the context of supply chain management …
by the structure and function of the human brain. In the context of supply chain management …
Graph neural networks for link prediction with subgraph sketching
Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link
Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to …
Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to …
How powerful are k-hop message passing graph neural networks
The most popular design paradigm for Graph Neural Networks (GNNs) is 1-hop message
passing---aggregating information from 1-hop neighbors repeatedly. However, the …
passing---aggregating information from 1-hop neighbors repeatedly. However, the …
Inductive representation learning in temporal networks via causal anonymous walks
Temporal networks serve as abstractions of many real-world dynamic systems. These
networks typically evolve according to certain laws, such as the law of triadic closure, which …
networks typically evolve according to certain laws, such as the law of triadic closure, which …
A machine learning approach for predicting hidden links in supply chain with graph neural networks
Supply chain business interruption has been identified as a key risk factor in recent years,
with high-impact disruptions due to disease outbreaks, logistic issues such as the recent …
with high-impact disruptions due to disease outbreaks, logistic issues such as the recent …
Equivariant and stable positional encoding for more powerful graph neural networks
Graph neural networks (GNN) have shown great advantages in many graph-based learning
tasks but often fail to predict accurately for a task-based on sets of nodes such as link/motif …
tasks but often fail to predict accurately for a task-based on sets of nodes such as link/motif …