1,665 research outputs found
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
The rise of graph-structured data such as social networks, regulatory
networks, citation graphs, and functional brain networks, in combination with
resounding success of deep learning in various applications, has brought the
interest in generalizing deep learning models to non-Euclidean domains. In this
paper, we introduce a new spectral domain convolutional architecture for deep
learning on graphs. The core ingredient of our model is a new class of
parametric rational complex functions (Cayley polynomials) allowing to
efficiently compute spectral filters on graphs that specialize on frequency
bands of interest. Our model generates rich spectral filters that are localized
in space, scales linearly with the size of the input data for
sparsely-connected graphs, and can handle different constructions of Laplacian
operators. Extensive experimental results show the superior performance of our
approach, in comparison to other spectral domain convolutional architectures,
on spectral image classification, community detection, vertex classification
and matrix completion tasks
The Law of Naval Warfare and China’s Maritime Militia
China operates a vast network of fishing vessels that form a maritime militia equipped and trained to conduct intelligence, communications, and targeting support for the People\u27s Liberation Army Navy. Fishing vessels normally are exempt from capture or attack in the law of naval warfare unless they are integrated into the naval forces, but distinguishing between legitimate fishing vessels and maritime militia during naval warfare is virtually impossible
ncRNA Classification with Graph Convolutional Networks
Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but
instead carry important biological functions. The task of ncRNA classification
consists in classifying a given ncRNA sequence into its family. While it has
been shown that the graph structure of an ncRNA sequence folding is of great
importance for the prediction of its family, current methods make use of
machine learning classifiers on hand-crafted graph features. We improve on the
state-of-the-art for this task with a graph convolutional network model which
achieves an accuracy of 85.73% and an F1-score of 85.61% over 13 classes.
Moreover, our model learns in an end-to-end fashion from the raw RNA graphs and
removes the need for expensive feature extraction. To the best of our
knowledge, this also represents the first successful application of graph
convolutional networks to RNA folding data
- …