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Found 3 papers, 2 papers with code
The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection
In this paper, we argue that the performance degradation is mainly attributed to the inconsistency between topology and attribute.
Improving Molecular Pretraining with Complementary Featurizations
Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry and drug discovery.
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT
This paper presented GTA, a new framework for multivariate time series anomaly detection that involves automatically learning a graph structure, graph convolution, and modeling temporal dependency using a Transformer-based architecture.
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