Google Scholar Paper Scraper

A Python-based scraping tool designed to extract research paper data from Google Scholar, specifically focused on Hyperspectral Image (HSI) Classification using Graph Neural Networks (GNNs).

📌 Overview

This repository contains a Jupyter Notebook (googlescholar_scraper.ipynb) that automates the collection of academic titles and authors. It is pre-configured to target the latest advancements (2020–present) in graph-based remote sensing.

🚀 Features

• Architecture-Specific Queries: Specialized search loops for GCN, GAT, and GraphSAGE models.
• Temporal Filtering: Automatically restricts results to publications from the year 2020 onwards.
• Clean Data Extraction: Parses HTML to isolate clean Paper Titles and Author/Source strings.
• Pagination Support: Scrapes multiple result pages (approx. 60 papers per query).
• Rate Limit Protection: Implements a time.sleep() delay to minimize the risk of IP blocking.

🛠️ Requirements

The script utilizes the following Python libraries:

• requests: For handling HTTP requests to Google Scholar.
• beautifulsoup4: For parsing the search result HTML.
• time: For managing request intervals.

📖 Usage

1. Open the notebook in Jupyter or Google Colab.
2. Define your search string in the query variable:

   query = "graph attention hyperspectral image classification"