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Found 5 papers, 3 papers with code
Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering
Consequently, our model benefits from a constant number of parameters and a constant-size memory footprint, allowing it to scale to considerably larger datasets.
PushNet: Efficient and Adaptive Neural Message Passing
We find that our models outperform competitors on all datasets in terms of accuracy with statistical significance.
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification
Node classification is one of the core tasks on attributed graphs, but successful graph learning solutions require sufficiently labeled data.
Semi-Supervised Learning on Graphs Based on Local Label Distributions
Precisely, we propose a new node embedding which is based on the class labels in the local neighborhood of a node.
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification
Malicious software (malware) poses an increasing threat to the security of communication systems as the number of interconnected mobile devices increases exponentially.
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