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Found 8 papers, 3 papers with code
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
Therefore, mini-batch training for graph transformers is a promising direction, but limited samples in each mini-batch can not support effective dense attention to encode informative representations.
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
We systematically review related works from the data to the computational aspects.
MentorGNN: Deriving Curriculum for Pre-Training GNNs
To comprehend heterogeneous graph signals at different granularities, we propose a curriculum learning paradigm that automatically re-weighs graph signals in order to ensure a good generalization in the target domain.
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning
Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships.
DISCO: Comprehensive and Explainable Disinformation Detection
Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous issues, such as political agendas and manipulating financial markets.
Deeper-GXX: Deepening Arbitrary GNNs
However, two major problems hinder the deeper GNNs to obtain satisfactory performance, i. e., vanishing gradient and over-smoothing.
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Metric Learning on Temporal Graphs via Few-Shot Examples
Graph metric learning methods aim to learn the distance metric over graphs such that similar graphs are closer and dissimilar graphs are farther apart.
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data
In the big data era, the relationship between entries becomes more and more complex.
