A comprehensive survey and experimental comparison of graph-based approximate nearest neighbor search
Approximate nearest neighbor search (ANNS) constitutes an important operation in a
multitude of applications, including recommendation systems, information retrieval, and …
multitude of applications, including recommendation systems, information retrieval, and …
Learning to simulate complex physics with graph networks
A Sanchez-Gonzalez, J Godwin… - International …, 2020 - proceedings.mlr.press
Here we present a machine learning framework and model implementation that can learn to
simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and …
simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and …
Multi-level cross-view contrastive learning for knowledge-aware recommender system
Knowledge graph (KG) plays an increasingly important role in recommender systems.
Recently, graph neural networks (GNNs) based model has gradually become the theme of …
Recently, graph neural networks (GNNs) based model has gradually become the theme of …
Node similarity preserving graph convolutional networks
Graph Neural Networks (GNNs) have achieved tremendous success in various real-world
applications due to their strong ability in graph representation learning. GNNs explore the …
applications due to their strong ability in graph representation learning. GNNs explore the …
[PDF][PDF] Deep graph structure learning for robust representations: A survey
Abstract Graph Neural Networks (GNNs) are widely used for analyzing graph-structured
data. Most GNN methods are highly sensitive to the quality of graph structures and usually …
data. Most GNN methods are highly sensitive to the quality of graph structures and usually …
Learning latent relations for temporal knowledge graph reasoning
Abstract Temporal Knowledge Graph (TKG) reasoning aims to predict future facts based on
historical data. However, due to the limitations in construction tools and data sources, many …
historical data. However, due to the limitations in construction tools and data sources, many …
Mining latent structures for multimedia recommendation
Multimedia content is of predominance in the modern Web era. Investigating how users
interact with multimodal items is a continuing concern within the rapid development of …
interact with multimodal items is a continuing concern within the rapid development of …
A survey on graph-based methods for similarity searches in metric spaces
Technology development has accelerated the volume growth of complex data, such as
images, videos, time series, and georeferenced data. Similarity search is a widely used …
images, videos, time series, and georeferenced data. Similarity search is a widely used …
A tale of two graphs: Freezing and denoising graph structures for multimodal recommendation
Multimodal recommender systems utilizing multimodal features (eg, images and textual
descriptions) typically show better recommendation accuracy than general recommendation …
descriptions) typically show better recommendation accuracy than general recommendation …
Efficient k-nearest neighbor graph construction for generic similarity measures
K-Nearest Neighbor Graph (K-NNG) construction is an important operation with many web
related applications, including collaborative filtering, similarity search, and many others in …
related applications, including collaborative filtering, similarity search, and many others in …