2019年2月25日 · In this paper, we study the problem from a completely different perspective, by introducing parallel flow decomposition of graphs. The essential idea is to decompose a graph …

We demonstrate that the our method, which we call GFCN (graph flow convolutional network), is able to transfer CNN architectures to general graphs. We demonstrate effectiveness of the …

CNN along each family of paths. We demonstrate that the our method, which we call GFCN (graph flow convolutional network), is able to transfer CNN architectures to general graphs …

GFCN : Graph Fairing Convolutional Networks for Anomaly Detection Graph convolution is a fundamental building block for many deep neural networks on graph-structured data. In this …

This repository is for paper GFCN: A New Graph Convolutional Network Based on Parallel Flows. To run the code of this repository, the following requriments are needed. Dowload the …

We demonstrate that the our method, which we call GFCN (graph flow convolutional network), is able to transfer CNN architectures to general graphs directly, unlike the spectral graph …

The essential idea is to decompose a graph into families of non-intersecting one dimensional (1D) paths, after which, we may apply a 1D CNN along each family of paths. We demonstrate that …

the our method, which we call GFCN (graph flow convolutional network), is able to transfer CNN architectures to general graphs. To show the effectiveness of our approach, we test our …

2024年1月1日 · Inspired by the implicit fairing concept in geometry processing for triangular mesh smoothing [17], we introduce a graph fairing convolutional network architecture, which we call …

through skip connections between layers, the proposed GFCN model is flexible and exploits both the graph structure and node features for learning discriminative node representations in an …