Research
Intelligent machines are changing our world. But how do these systems discover meaningful patterns in complex information, help us explore new ideas, and inspire creative thinking? I study how machines and humans can team up to uncover hidden connections, generate fresh insights, and push the boundaries of knowledge—transforming complicated data into simple, powerful understandings.
- Keywords: Network Science, Representation Learning, Science of Science, Human-Computer Interaction
Recent work
Network community detection via neural embeddings
Our paper on the detectability limit of neural embeddings is finally out from @NatureComms! We showed that a simple shallow neural net w/o non-linear activation can achieve the optimal community detectability limit. Let's dive in! @santo_fortunato @filrad https://t.co/DJM1NgDEfl
— Sadamori Kojaku (@skojaku) November 8, 2024
Implicit degree bias in the link prediction task
🚨Paper Alert🚨 Benchmarks guide #MachineLearning, but is the core benchmark for #GraphML, the link prediction task, guiding us correctly? With @RachithAiyappa @VisonWang1 @ozgurcanseckin @snetsMJ @JisungYoon8 and YY Ahn, we question its validity. Dive in! https://t.co/g5jQRC1yHY
— Sadamori Kojaku (@skojaku) May 28, 2024