Fractional Graph Convolutional Networks (FGCN) for Semi-Supervised Learning
Authors: Yuzhou Chen, Yulia R. Gel, Konstantin Avrachenkov
Keywords: convolutional networks, node classification, Levy flight, graph-based semi-supervised learning, local graph topology
TL;DR: A new Fractional Generalized Graph Convolutional Networks (FGCN) method for semi-supervised learning
Abstract: Due to high utility in many applications, from social networks to blockchain to power grids, deep learning on non-Euclidean objects such as graphs and manifolds continues to gain an ever increasing interest. Most currently available techniques are based on the idea of performing  a convolution operation in the spectral domain with a suitably chosen nonlinear trainable filter and then approximating the filter with finite order polynomials. However, such polynomial approximation approaches tend to be both non-robust to changes in the graph structure and to capture primarily the global graph topology. In this paper we propose a new Fractional Generalized Graph Convolutional Networks (FGCN) method for semi-supervised learning, which casts the L\'evy Fights into random walks on graphs and, as a result, allows to more accurately account for the intrinsic graph topology and to substantially improve classification performance, especially for  heterogeneous graphs.