Fractional Graph Convolutional Networks (FGCN) for Semi-Supervised Learning

Yuzhou Chen, Yulia R. Gel, Konstantin Avrachenkov

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.

Code: https://www.dropbox.com/sh/ajtz6inf677nkcv/AACXkFRZjRrCkxYkxJDNfks0a?dl=0.

Keywords: convolutional networks, node classification, Levy flight, graph-based semi-supervised learning, local graph topology