TLDR: This story gives a high-level entry to graph neural network: the How and the Why, before introducing a serious issue that accompanies the message passing framework which represents the main feature of today’s GNN. Don’t forget to use the references below to deepen your understanding of GNNs! An illustrated guide to Graph neural networks Graph neural…

Extracting Temporal dynamics in large research corpus — Introduction Research knowledge has always been document-based (research papers, reviews), and many entities like Semantic scholar or Arxiv tried to aggregate this text knowledge in a big corpus where you can use keywords or IDs(DOI codes) to access specific paper or get inquiries about the state of a specific field, thus…

Note: This is my second article sharing personal reflections about things I do at work or in my free time. No idea here is new for sure, but I usually treat open writing as a way to push my thinking boundaries and discover new perspectives. …

Disclaimer: This article and the ones coming soon are just reflections, and summarize my personal perspective, which needs to be updated by your comments. …

Notes on different centrality measures: definition and tradeoffs — Introduction Centrality measures are an important tool to analyze your network, whatever its a national road network and you want to determine major cities or its an electrical web and you want to identify critical nodes that need to be maintained in order to eliminate any future disturbance to your network…

A series of easy to grasp notes on graph theory for curious learners — Why am I creating this notes? As a curious learner, I find myself consuming an important quantity of material in order to stay updated in my favorite fields or build a solid base to enter new ones. …