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LFGCN: Levitating over Graphs with Levy Flights

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, coined Geometric Deep Learning (GDL), continues to gain an ever increasing interest. We propose a new Lévy Flights Graph Convolutional Networks (LFGCN) method for semi-supervised learning, which casts the Lévy Flights into random walks on graphs and, as a result, allows both to accurately account for the intrinsic graph topology and to substantially improve classification performance, especially for heterogeneous graphs. Furthermore, we propose a new preferential P-DropEdge method based on the Girvan-Newman argument. That is, in contrast to uniform removing of edges as in DropEdge, following the Girvan-Newman algorithm, we detect network periphery structures using information on edge betweenness and then remove edges according to their betweenness centrality. Our experimental results on semi-supervised node classification tasks demonstrate that the LFGCN coupled with P-DropEdge accelerates the training task, increases stability and further improves predictive accuracy of learned graph topology structure. Finally, in our case studies we bring the machinery of LFGCN and other deep networks tools to analysis of power grid networks - the area where the utility of GDL remains untapped.
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Contributor : Konstantin Avrachenkov Connect in order to contact the contributor
Submitted on : Thursday, January 28, 2021 - 10:21:44 AM
Last modification on : Thursday, January 20, 2022 - 4:17:43 PM
Long-term archiving on: : Thursday, April 29, 2021 - 6:24:18 PM


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Distributed under a Creative Commons Attribution 4.0 International License


  • HAL Id : hal-03123684, version 1
  • ARXIV : 2009.02365



Yuzhou Chen, Yulia Gel, Konstantin Avrachenkov. LFGCN: Levitating over Graphs with Levy Flights. IEEE ICDM 2020 - International Conference on Data Mining, Nov 2020, Sorrento, Italy. ⟨hal-03123684⟩



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