How Computers Learn Hidden Patterns in Networks
Imagine a map of friends, streets, or web pages where each dot links to others — that is a network.
Scientists made a simple idea: make two slightly changed copies of the map, hide or shuffle some parts, then teach a computer to spot what stays the same.
The computer looks for matching bits between the copies, it build a small picture for every dot so similar dots get closer.
This approach trains itself, needs no labels, so it’s called unsupervised, and that makes it useful where no one has tagged data.
In many tests this way gave better results than methods that used labels, sometimes even beating those trained by people.
You can use it to find missing links, spot big hubs, or reveal groups in social sites, biology maps, or web data.
It’s simple to run, fast enough for large maps, and often finds patterns people miss.
By comparing two views of the same network the system learns what truly matters, and that could change how we explore connected data.
Read article comprehensive review in Paperium.net:
Deep Graph Contrastive Representation Learning
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