Graph structure learning
52 papers with code • 1 benchmarks • 2 datasets
Semi-supervised node classification when a graph structure is not available.
Libraries
Use these libraries to find Graph structure learning models and implementations
Cora
ChEMBL
Most implemented papers
Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer
gicheonkang/sglkt-visdial
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Findings (EMNLP) 2021
Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context.
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
llan-ml/MetaTNE
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NeurIPS 2020
We study the problem of node classification on graphs with few-shot novel labels, which has two distinctive properties: (1) There are novel labels to emerge in the graph; (2) The novel labels have only a few representative nodes for training a classifier.
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
DM2-ND/CoEvoGNN
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In this work, we present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence.
Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture
Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation.
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model.
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
BorealisAI/SLAPS-GNN
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NeurIPS 2021
In this work, we propose the Simultaneous Learning of Adjacency and GNN Parameters with Self-supervision, or SLAPS, a method that provides more supervision for inferring a graph structure through self-supervision.
Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling
The characterisation of the brain as a functional network in which the connections between brain regions are represented by correlation values across time series has been very popular in the last years.
An Empirical Study: Extensive Deep Temporal Point Process
bird-tao/edtpp
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In this paper, we first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process, which can be concluded into four fields: encoding of history sequence, formulation of conditional intensity function, relational discovery of events and learning approaches for optimization.
Path Signature Area-Based Causal Discovery in Coupled Time Series
Path signatures and their associated signed areas provide a new way to approach the analysis of causally linked dynamical systems, particularly in informing a model-free, data-driven approach to algorithmic causal discovery.
Graph Structure Learning with Variational Information Bottleneck
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.
