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Found 6 papers, 2 papers with code
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data.
Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs
Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily.
Self-supervision meets kernel graph neural models: From architecture to augmentations
Recently, the idea of designing neural models on graphs using the theory of graph kernels has emerged as a more transparent as well as sometimes more expressive alternative to MPNNs known as kernel graph neural networks (KGNNs).
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensionality to learn informative and discriminative full-precision representations, raising concerns about computation, memory footprint, and energy consumption burden (largely overlooked) for real-world applications.
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones.
DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Risk
Graph representation plays an important role in the field of financial risk control, where the relationship among users can be constructed in a graph manner.
