IEEE

sigRGCN: A Robust Residual Graph Convolutional Network for scRNA-Seq Data Clustering

Abstract: Clustering is a crucial step in single-cell RNA sequencing (scRNA-seq) data analysis, facilitating the discovery of new cell types and the grouping of similar cells. Recently, graph ...
Mount Desert Islander

Town data shines light on lodging, housing in Bar Harbor

BAR HARBOR — How has the lodging industry changed over time? And how has it impacted other aspects of life in town, especially the fragile housing supply? These are some of the questions that town ...
IEEE

VisCARS: Knowledge Graph-Based Context-Aware Recommender System for Time-Series Data Visualization and Monitoring Dashboards

Abstract: Data visualization recommendation aims to assist the user in creating visualizations from a given dataset. The process of creating appropriate visualizations requires expert knowledge of the ...
University of Wyoming

Dice Roll Bar Graph

This module uses random number generators to simulate dice rolls. The user can provide any number of different sided dice and the number of times the dice are rolled. The program will then produce a ...
GitHub

A framework for transforming tabular (CSV, SQL) and hierarchical data (JSON, XML) into property graphs and ingesting them into graph databases (ArangoDB, Neo4j).

⚠️ Package Renamed: This package was formerly known as graphcast. [Rest of your existing README content...] ...
unite

What Every Data Scientist Should Know About Graph Transformers and Their Impact on Structured Data

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...
GitHub

Graph Neural Networks for Tabular Data Learning (GNN4TDL)

The deep learning-based approaches to Tabular Data Learning (TDL), classification and regression, have shown competing performance, compared to their conventional counterparts. However, the latent ...