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Found 7 papers, 6 papers with code
DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization
Graph clustering is a fundamental and challenging task in the field of graph mining where the objective is to group the nodes into clusters taking into consideration the topology of the graph.
Link Prediction without Graph Neural Networks
Link prediction, which consists of predicting edges based on graph features, is a fundamental task in many graph applications.
Global Counterfactual Explainer for Graph Neural Networks
One way to address this is counterfactual reasoning where the objective is to change the GNN prediction by minimal changes in the input graph.
Graph Neural Diffusion Networks for Semi-supervised Learning
To solve these two issues, we propose a new graph neural network called GND-Nets (for Graph Neural Diffusion Networks) that exploits the local and global neighborhood information of a vertex in a single layer.
SECP-Net: SE-Connection Pyramid Network of Organ At Risk Segmentation for Nasopharyngeal Carcinoma
SECP-Net extracts global and multi-size information flow with se connection (SEC) modules and a pyramid structure of network for improving the segmentation performance, especially that of small organs.
A Broader Picture of Random-walk Based Graph Embedding
Graph embedding based on random-walks supports effective solutions for many graph-related downstream tasks.
POLE: Polarized Embedding for Signed Networks
From the 2016 U. S. presidential election to the 2021 Capitol riots to the spread of misinformation related to COVID-19, many have blamed social media for today's deeply divided society.
