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 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. We demonstrate effectiveness of the method with synthetic and real applications.

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 directly, unlike the spectral graph methods. By incorporating skip mechanisms, we show that GFCN recovers some of the spectral graph methods.

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 paper, we introduce a simple, yet very effective graph convolutional network with skip connections for semi-supervised anomaly detection.

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 methods. By incorporating skip mechanisms, we show that GFCN recovers some of …

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.

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 method on the classical MNIST dataset, synthetic datasets on network information propagation and a news article classification dataset. Index Terms

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 GFCN, for deep semi-supervised anomaly detection. In addition to performing graph convolution, GFCN uses a skip connection to combine both the initial node ...

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 effort to detect anomalies in a semi-supervised setting. Not only does GFCN outperforms strong anomaly detection baselines,