Data augmentation for deep graph learning: A survey
Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …
demonstrated remarkable performance on numerous graph learning tasks. To address the …
A survey on fairness for machine learning on graphs
Nowadays, the analysis of complex phenomena modeled by graphs plays a crucial role in
many real-world application domains where decisions can have a strong societal impact …
many real-world application domains where decisions can have a strong societal impact …
Learning to reconstruct missing data from spatiotemporal graphs with sparse observations
Modeling multivariate time series as temporal signals over a (possibly dynamic) graph is an
effective representational framework that allows for developing models for time series …
effective representational framework that allows for developing models for time series …
Attribute-missing graph clustering network
Deep clustering with attribute-missing graphs, where only a subset of nodes possesses
complete attributes while those of others are missing, is an important yet challenging topic in …
complete attributes while those of others are missing, is an important yet challenging topic in …
VilLain: Self-supervised learning on hypergraphs without features via virtual label propagation
Group interactions arise in various scenarios in real-world systems: collaborations of
researchers, co-purchases of products, and discussions in online Q&A sites, to name a few …
researchers, co-purchases of products, and discussions in online Q&A sites, to name a few …
[HTML][HTML] Efficient anatomical labeling of pulmonary tree structures via deep point-graph representation-based implicit fields
Pulmonary diseases rank prominently among the principal causes of death worldwide.
Curing them will require, among other things, a better understanding of the complex 3D tree …
Curing them will require, among other things, a better understanding of the complex 3D tree …
Multi-view graph imputation network
Graph data in the real world is often accompanied by the problem of missing attributes.
Recently, self-supervised graph representation learning, implementing data imputation …
Recently, self-supervised graph representation learning, implementing data imputation …
Advective diffusion transformers for topological generalization in graph learning
Graph diffusion equations are intimately related to graph neural networks (GNNs) and have
recently attracted attention as a principled framework for analyzing GNN dynamics …
recently attracted attention as a principled framework for analyzing GNN dynamics …
Graph Neural Network-Based WiFi Indoor Localization System With Access Point Selection
With the popularity of mobile devices and the increasing demand for indoor localization
services, the localization of indoor mobile users is becoming more and more popular …
services, the localization of indoor mobile users is becoming more and more popular …
[HTML][HTML] Learnable topological features for phylogenetic inference via graph neural networks
C Zhang - ArXiv, 2023 - ncbi.nlm.nih.gov
Structural information of phylogenetic tree topologies plays an important role in phylogenetic
inference. However, finding appropriate topological structures for specific phylogenetic …
inference. However, finding appropriate topological structures for specific phylogenetic …