Interferometric Graph Transform: a Deep Unsupervised Graph Representation
This work addresses the challenge of building effective unsupervised graph representations for various domains, though it appears incremental as it builds on spectral methods.
The authors tackled the problem of unsupervised graph representation learning by proposing the Interferometric Graph Transform (IGT), a deep unsupervised graph convolutional neural network, which achieved new state-of-the-art results in tasks like image classification, community detection, and action recognition.
We propose the Interferometric Graph Transform (IGT), which is a new class of deep unsupervised graph convolutional neural network for building graph representations. Our first contribution is to propose a generic, complex-valued spectral graph architecture obtained from a generalization of the Euclidean Fourier transform. We show that our learned representation consists of both discriminative and invariant features, thanks to a novel greedy concave objective. From our experiments, we conclude that our learning procedure exploits the topology of the spectral domain, which is normally a flaw of spectral methods, and in particular our method can recover an analytic operator for vision tasks. We test our algorithm on various and challenging tasks such as image classification (MNIST, CIFAR-10), community detection (Authorship, Facebook graph) and action recognition from 3D skeletons videos (SBU, NTU), exhibiting a new state-of-the-art in spectral graph unsupervised settings.