22,017 research outputs found
Structure fusion based on graph convolutional networks for semi-supervised classification
Suffering from the multi-view data diversity and complexity for
semi-supervised classification, most of existing graph convolutional networks
focus on the networks architecture construction or the salient graph structure
preservation, and ignore the the complete graph structure for semi-supervised
classification contribution. To mine the more complete distribution structure
from multi-view data with the consideration of the specificity and the
commonality, we propose structure fusion based on graph convolutional networks
(SF-GCN) for improving the performance of semi-supervised classification.
SF-GCN can not only retain the special characteristic of each view data by
spectral embedding, but also capture the common style of multi-view data by
distance metric between multi-graph structures. Suppose the linear relationship
between multi-graph structures, we can construct the optimization function of
structure fusion model by balancing the specificity loss and the commonality
loss. By solving this function, we can simultaneously obtain the fusion
spectral embedding from the multi-view data and the fusion structure as
adjacent matrix to input graph convolutional networks for semi-supervised
classification. Experiments demonstrate that the performance of SF-GCN
outperforms that of the state of the arts on three challenging datasets, which
are Cora,Citeseer and Pubmed in citation networks
Relational Fusion Networks: Graph Convolutional Networks for Road Networks
The application of machine learning techniques in the setting of road
networks holds the potential to facilitate many important intelligent
transportation applications. Graph Convolutional Networks (GCNs) are neural
networks that are capable of leveraging the structure of a network. However,
many implicit assumptions of GCNs do not apply to road networks. We introduce
the Relational Fusion Network (RFN), a novel type of GCN designed specifically
for road networks. In particular, we propose methods that outperform
state-of-the-art GCNs by 21%-40% on two machine learning tasks in road
networks. Furthermore, we show that state-of-the-art GCNs may fail to
effectively leverage road network structure and may not generalize well to
other road networks.Comment: IEEE Transactions on Intelligent Transportation Systems (2020). arXiv
admin note: substantial text overlap with arXiv:1908.1156
Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images
Graph convolutional neural networks have shown significant potential in
natural and histopathology images. However, their use has only been studied in
a single magnification or multi-magnification with late fusion. In order to
leverage the multi-magnification information and early fusion with graph
convolutional networks, we handle different embedding spaces at each
magnification by introducing the Multi-Scale Relational Graph Convolutional
Network (MS-RGCN) as a multiple instance learning method. We model
histopathology image patches and their relation with neighboring patches and
patches at other scales (i.e., magnifications) as a graph. To pass the
information between different magnification embedding spaces, we define
separate message-passing neural networks based on the node and edge type. We
experiment on prostate cancer histopathology images to predict the grade groups
based on the extracted features from patches. We also compare our MS-RGCN with
multiple state-of-the-art methods with evaluations on several source and
held-out datasets. Our method outperforms the state-of-the-art on all of the
datasets and image types consisting of tissue microarrays, whole-mount slide
regions, and whole-slide images. Through an ablation study, we test and show
the value of the pertinent design features of the MS-RGCN
- …