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 …
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
EE Kosasih, A Brintrup - International Journal of Production …, 2022 - Taylor & Francis
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 …
Global self-attention as a replacement for graph convolution
We propose an extension to the transformer neural network architecture for general-purpose
graph learning by adding a dedicated pathway for pairwise structural information, called …
graph learning by adding a dedicated pathway for pairwise structural information, called …
On positional and structural node features for graph neural networks on non-attributed graphs
Graph neural networks (GNNs) have been widely used in various graph-related problems
such as node classification and graph classification, where the superior performance is …
such as node classification and graph classification, where the superior performance is …
Towards knowledge graph reasoning for supply chain risk management using graph neural networks
Modern supply chains are complex, interconnected systems that contain emergent, invisible
dependencies. Lack of visibility often hinders effective risk planning and results in delayed …
dependencies. Lack of visibility often hinders effective risk planning and results in delayed …
Neural link prediction with walk pooling
L Pan, C Shi, I Dokmanić - arXiv preprint arXiv:2110.04375, 2021 - arxiv.org
Graph neural networks achieve high accuracy in link prediction by jointly leveraging graph
topology and node attributes. Topology, however, is represented indirectly; state-of-the-art …
topology and node attributes. Topology, however, is represented indirectly; state-of-the-art …
Graph neural networks for efficient learning of mechanical properties of polycrystals
We present graph neural networks (GNNs) as an efficient and accurate machine learning
approach to predict mechanical properties of polycrystalline materials. Here, a GNN was …
approach to predict mechanical properties of polycrystalline materials. Here, a GNN was …