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Found 9 papers, 4 papers with code
Putting Context in Context: the Impact of Discussion Structure on Text Classification
We also experiment with different amounts of training data and analyse the topology of local discussion networks in a privacy-compliant way.
Meta-Path Learning for Multi-relational Graph Neural Networks
Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or they rely on handcrafted chains of relational dependencies, called meta-paths.
A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction
Then, we convert the tree representation of the source code to a Flow Augmented-AST graph (FA-AST) representation.
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data.
Explaining the Explainers in Graph Neural Networks: a Comparative Study
Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process.
Global Explainability of GNNs via Logic Combination of Learned Concepts
While instance-level explanation of GNN is a well-studied problem with plenty of approaches being developed, providing a global explanation for the behaviour of a GNN is much less explored, despite its potential in interpretability and debugging.
TEP-GNN: Accurate Execution Time Prediction of Functional Tests using Graph Neural Networks
TEP-GNN uses FA-ASTs, or flow-augmented ASTs, as a graph-based code representation approach, and predicts test execution times using a powerful graph neural network (GNN) deep learning model.
Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets
State of the art Graph Neural Networks (GNNs) are used to extract information from the Tweet-MLN and make predictions based on the extracted graph features.
Generating Synthetic Mobility Networks with Generative Adversarial Networks
We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks.
