Scrapping Paper digest Highlights
Published:
Quick tool to scrape the latest machine learning conferences and perform keyword searches. The code returns a display of article titles and a short text describing the main contributions of the article.
The code is available here
Scrap Paper Digest Highlights conf
Step 1: Go to Conference Paper Digests by Year
Select the url of the conference you want scrap.
Example ICML 2022 Conference : https://www.paperdigest.org/2022/07/icml-2022-highlights/
Step 2 : Run the program scrapper with your key words
Search the key words ‘graph’ AND ‘dynamic’ only in the title
python scrap_paper_digest.py --url https://www.paperdigest.org/2022/07/icml-2022-highlights/ -w graph dynamic --all > dynamic.txt
Search the key words ‘graph’ OR ‘dynamic’ only in the title
python scrap_paper_digest.py --url https://www.paperdigest.org/2022/07/icml-2022-highlights/ -w graph dynamic > dynamic.txt
Search the key words ‘graph’ OR ‘dynamic’ in the title AND in the highlights
python scrap_paper_digest.py --url https://www.paperdigest.org/2022/07/icml-2022-highlights/ -w graph dynamic --search_highlights > dynamic.txt
Results example:
python scrap_paper_digest.py --url https://www.paperdigest.org/2022/04/www-2022-highlights/ -w graph dynamic --all --search_highlights
- A New Dynamic Algorithm for Densest Subhypergraphs
Highlight: This algorithm worked only on unweighted hypergraphs, and had an approximation ratio of (1 +?)r2 and an update time of O(poly(r, log?n)), where r denotes the maximum rank of the input across all the updates. We obtain a new algorithm for this problem, which works even when the input hypergraph is weighted.
- TREND: TempoRal Event and Node Dynamics for Graph Representation Learning
Highlight: In this work, We propose TREND, a novel framework for temporal graph representation learning, driven by TempoRal Event and Node Dynamics and built upon a Hawkes process-based graph neural network (GNN).
- Multimodal Continual Graph Learning with Neural Architecture Search
Highlight: However, considering multimodal continual graph learning with evolving topological structures poses great challenges: i) it is unclear how to incorporate the multimodal information into continual graph learning and ii) it is nontrivial to design models that can capture the structure-evolving dynamics in continual graph learning. To tackle these challenges, in this paper we propose a novel Multimodal Structure-evolving Continual Graph Learning (MSCGL) model, which continually learns both the model architecture and the corresponding parameters for Adaptive Multimodal Graph Neural Network (AdaMGNN).
3 Papers found matching your search