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Found 6 papers, 1 papers with code
StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning
This allows the encoder not to perform any message passing during the training stage, and significantly reduces the number of sample pairs in the contrastive loss.
Towards Understanding Neural Collapse: The Effects of Batch Normalization and Weight Decay
Neural Collapse (NC) is a geometric structure recently observed in the final layer of neural network classifiers.
Graph-Level Embedding for Time-Evolving Graphs
We evaluate our proposed model on five publicly available datasets for the task of temporal graph similarity ranking, and our model outperforms baseline methods.
Learning-Augmented B-Trees
We study learning-augmented binary search trees (BSTs) and B-Trees via Treaps with composite priorities.
Provable Lifelong Learning of Representations
We prove that for any desired accuracy on all tasks, the dimension of the representation remains close to that of the underlying representation.
Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization
Recent years have seen a rise in the development of representational learning methods for graph data.
