1 citations · 1 across the 3 of their papers we have counts for
3 papers
Graph Mining under Data scarcity
Appan Rakaraddi, Lam Siew-Kei, Mahardhika Pratama +1
Multitude of deep learning models have been proposed for node classification in graphs. However, they tend to perform poorly under labeled-data scarcity. Although Few-shot learning…
Reinforced Continual Learning for Graphs
Appan Rakaraddi, Siew Kei Lam, Mahardhika Pratama +1
Graph Neural Networks (GNNs) have become the backbone for a myriad of tasks pertaining to graphs and similar topological data structures. While many works have been established in…
Unsupervised Learning for Identifying High Eigenvector Centrality Nodes: A Graph Neural Network Approach
Appan Rakaraddi, Mahardhika Pratama
The existing methods to calculate the Eigenvector Centrality(EC) tend to not be robust enough for determination of EC in low time complexity or not well-scalable for large networks…