Graphs are a ubiquitous data structure and a universal language for representing objects and complex interactions. They can model a wide range of real-world systems, such as social networks, chemical ...

A graph structure is extremely useful for predicting properties of its constituents. The most successful way of performing this prediction is to map each entity to a vector through the use of deep ...

Giulia Livieri sets out remarkable new research with results that clarify how learning works on complex graphs and how quickly any method (including Graph Convolutional Networks) can learn from them, ...

A super geeky topic, which could have super important repercussions in the real world. That description could very well fit anything from cold fusion to knowledge graphs, so a bit of unpacking is in ...

Here's the new description with all links and accompanying text removed: Learn how to determine increasing/decreasing intervals. There are many ways in which we can determine whether a function is ...