Probabilistic and Regularized Graph Convolutional Networks (L42 Michaelmas 2017)
نویسنده
چکیده
This paper explores the recently proposed Graph Convolutional Network architecture proposed in (Kipf & Welling, 2016) The key points of their work is summarized and their results are reproduced. Graph regularization and alternative graph convolution approaches are explored. I find that explicit graph regularization was correctly rejected by (Kipf & Welling, 2016). I attempt to improve the performance of GCN by approximating a k-step transition matrix in place of the normalized graph laplacian, but I fail to find positive results. Nonetheless, the performance of several configurations of this GCN variation is shown for the Cora, Citeseer, and Pubmed datasets.
منابع مشابه
LPKP: location-based probabilistic key pre-distribution scheme for large-scale wireless sensor networks using graph coloring
Communication security of wireless sensor networks is achieved using cryptographic keys assigned to the nodes. Due to resource constraints in such networks, random key pre-distribution schemes are of high interest. Although in most of these schemes no location information is considered, there are scenarios that location information can be obtained by nodes after their deployment. In this paper,...
متن کاملToward Robustness against Label Noise in Training Deep Discriminative Neural Networks
Collecting large training datasets, annotated with high-quality labels, is costly and time-consuming. This paper proposes a novel framework for training deep convolutional neural networks from noisy labeled datasets that can be obtained cheaply. The problem is formulated using an undirected graphical model that represents the relationship between noisy and clean labels, trained in a semisupervi...
متن کاملImage Retrieval Using Deep Convolutional Neural Networks and Regularized Locality Preserving Indexing Strategy
Convolutional Neural Networks (CNN) has been a very popular area in large scale data processing and many works have demonstrate that CNN is a very promising tool in many field, e.g., image classification and image retrieval. Theoretically, CNN features can become better and better with the increase of CNN layers. But on the other side more layers can dramatically increase the computational cost...
متن کاملGraph Convolutional Networks
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden lay...
متن کاملDiffusion-Convolutional Neural Networks
We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffusion-based representations can be learned from graphstructured data and used as an effective basis for node classification. DCNNs have several attractive qualities, including a latent representation for graphical data ...
متن کامل