ABSTRACT: Government procurement contracts can be complicated, with extensive risk analysis and compliance reviews. The traditional methods of contract analytics are time-consuming and often inexact, ...
Collective Inference (CI) is a procedure designed to boost weak relational classifiers, specially for node classification tasks. Graph Neural Networks (GNNs) are strong classifiers that have been used ...
但截至2025年8月，似乎会报警告： [WARNING] Network and explorer-specific api keys are deprecated in favour of the new Etherscan v2 api. Support for v1 is expected to end by May 31st, 2025. To migrate, please ...
ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...
Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...
Abstract: In the node classification task, it is natural to presume that densely connected nodes tend to exhibit similar attributes. Given this, it is crucial to first define what constitutes a dense ...
I think the method you proposed is very meaningful. And, I am trying to explain other node classification graph datasets using GLGExplainer. But I can't find where to replace the dataset. I would like ...
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