Graph Transformer for Node Label Prediction with PyG

Updated on January 29, 2024 | in Data Science

In this tutorial, we attempt to identify the field of a paper based on its citation graph, which serves as a proxy for the paper’s content and its context within the scientific discourse. Understanding and leveraging this structure is central to our ML problem: how to infer the field of a paper from the complex web of citations in which it is embedded.

A Short Review on Graph Neural Networks: Mechanism and Implementation

Updated on September 14, 2022 | in 2. Knowledge Network / Network Science

Graphical Neural Networks (GNNs) are a family of neural networks that can operate naturally on graphically structured data. They provide an effective way to do node-level, edge-level, and graph-level prediction tasks by extracting and exploiting features from the underlying graph.

A Short Review of Scholarly Network Approahces to Modeling the Structure of Knowledge Domains

Updated on April 19, 2022 | in 2. Knowledge Network / Network Science

Scholarly networks, built from academic metadata, have proved powerful tools in these tasks. Its nodes usually denotes academic entities (concept, paper, venue, researcher, institution, etc.) and links represent relationships such as citation, co-authorship, or co-word.

Evaluate the Effectiveness of Scientific Map (Scholarly Network) Visualizations

Updated on April 4, 2022 | in 2. Knowledge Network / Network Science

Science mapping aims to reveal the structure and dynamics of academic research by providing a holistic view of the knowledge domain, i.e., the distribution and connections of academic units, such as topics, publications, journals, and researchers.

Knowledge Graph Pipeline: from Construction to Application

Updated on December 13, 2021 | in 2. Knowledge Network / Network Science

Raw data → structured data (maintain and evolve) → graph algorithm and model → application

Project | Optimize Supportive Vector Machine and Least Square Regression Models for Traffic Flow Prediction

Updated on October 22, 2021 | in Data Science

This paper adopted Supportive Vector Regression (SVR) and Least Square Regression(LSR) models to predict the traffic flow. LSR model and SVR models with linear, Gaussian, polynomial kernel separately are built and then evaluated with MSE and R-squared.