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Found 5 papers, 2 papers with code
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks
Motivated by the aim of providing global explanations, we adapt the well-known Automated Concept-based Explanation approach (Ghorbani et al., 2019) to GNN node and graph classification, and propose GCExplainer.
Graph Representation Learning on Tissue-Specific Multi-Omics
Overall, the combination of RNA-sequencing and gene methylation data leads to a link prediction accuracy of 71% on GGI networks.
Quantum Graph Neural Networks
We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and are particularly suitable to be executed on distributed quantum systems over a quantum network.
Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Prediction
Disease-gene prediction (DGP) refers to the computational challenge of predicting associations between genes and diseases.
Replace or Retrieve Keywords In Documents at Scale
For a document of size N (characters) and a dictionary of M keywords, the time complexity will be O(N).
Data Structures and Algorithms
