Search Results for author:
Found 5 papers, 2 papers with code
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs.
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
The superior performance of GNNs often correlates with the availability and quality of node-level features in the input networks.
A Survey of Graph Unlearning
By laying a solid foundation and fostering continued progress, this survey seeks to inspire researchers to further advance the field of graph unlearning, thereby instilling confidence in the ethical growth of AI systems and reinforcing the responsible application of machine learning techniques in various domains.
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
We delve deeply into the dataset generation search space by crafting 35 datasets that encompass static and dynamic brain connectivity, running in excess of 15 baseline methods for benchmarking.
Sequential Graph Neural Networks for Source Code Vulnerability Identification
However, this task is rather challenging owing to the absence of reliable and adequately managed datasets and learning models.
