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Found 13 papers, 6 papers with code
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed.
Collective Relational Inference for learning heterogeneous interactions
First, it infers the interaction types of different edges collectively by explicitly encoding the correlation among incoming interactions with a joint distribution, and second, it allows handling systems with variable topological structure over time.
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection
Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inference with graph neural networks (GNNs), which is useful to catch multihop risk propagation in a transaction graph.
Modelling graph dynamics in fraud detection with "Attention"
At online retail platforms, detecting fraudulent accounts and transactions is crucial to improve customer experience, minimize loss, and avoid unauthorized transactions.
Learning Physics-Consistent Particle Interactions
Access to the governing particle interaction law is fundamental for a complete understanding of such systems.
Graph Neural Networks in Real-Time Fraud Detection with Lambda Architecture
Transaction checkout fraud detection is an essential risk control components for E-commerce marketplaces.
Suspicious Massive Registration Detection via Dynamic Heterogeneous Graph Neural Networks
Massive account registration has raised concerns on risk management in e-commerce companies, especially when registration increases rapidly within a short time frame.
xFraud: Explainable Fraud Transaction Detection
At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss.
Adversarial Attack on Community Detection by Hiding Individuals
It has been demonstrated that adversarial graphs, i. e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks.
DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition
Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.
Word embedding re-examined: is the symmetrical factorization optimal?
Such linear transformation will result in these good properties.
Predicting Path Failure In Time-Evolving Graphs
Through experiments on a real-world telecommunication network and a traffic network in California, we demonstrate the superiority of LRGCN to other competing methods in path failure prediction, and prove the effectiveness of SAPE on path representation.
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Many interesting problems in machine learning are being revisited with new deep learning tools.
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