ICML 2019 is coming up soon, and I plan to be there (except I’m missing Tuesday). I want to briefly tout the excellent work of a fantastic student who joined our lab, Sami Abu-El-Haija.  If you’ve kept up with develops on learning with graphs, you may be aware of graph convolutional networks, which combine the best of neural networks and spectral learning on graphs to produce some top-notch results on graph datasets. There is one drawback of the original graph convolution approach. Unlike visual convolutions, it doesn’t allow for complex weighting of pixels at different locations within the kernel. Basically, the equivalent relative weighting in graph convolutions are constant, and this makes it difficult to distinguish how neighbors in a graph might systematically differ in their effect from neighbors of neighbors. Sami’s paper MixHop rectifies this issue and shows that this gives some nice performance boosts. Sami will be presenting the work at ICML and code for the approach is also available on github.

MixHop architecture