Graph structure learning
53 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
Towards Unsupervised Deep Graph Structure Learning
To solve the unsupervised GSL problem, we propose a novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning.
Evidence-aware Fake News Detection with Graph Neural Networks
CRIPAC-DIG/GET
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In this paper, we focus on the evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i. e., a claim).
Balanced Graph Structure Learning for Multivariate Time Series Forecasting
niuffs/LSCGF
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However, current models do not incorporate the trade-off between efficiency and flexibility and lack the guidance of domain knowledge in the design of graph structure learning algorithms.
Mold into a Graph: Efficient Bayesian Optimization over Mixed-Spaces
jjaeyeon/gebo
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Real-world optimization problems are generally not just black-box problems, but also involve mixed types of inputs in which discrete and continuous variables coexist.
Boosting Graph Structure Learning with Dummy Nodes
hkust-knowcomp/dummynode4graphlearning
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We extend graph kernels and graph neural networks with dummy nodes and conduct experiments on graph classification and subgraph isomorphism matching tasks.
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs.
Robust Graph Structure Learning via Multiple Statistical Tests
A natural way to construct a graph among images is to treat each image as a node and assign pairwise image similarities as weights to corresponding edges.
Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks
Comprehensive experiments have demonstrated the superiority of GETRAL over the state-of-the-arts and validated the efficacy of semantic mining with graph structure and contrastive learning.
Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting
alipay/rgsl
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In this paper, we propose Regularized Graph Structure Learning (RGSL) model to incorporate both explicit prior structure and implicit structure together, and learn the forecasting deep networks along with the graph structure.
HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding
With the tree-likeness property of hyperbolic space, the underlying semantic hierarchy among words and topics can be better exploited to mine more interpretable topics.
