94 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
LFGCN: Levitating over Graphs with Levy Flights
Due to high utility in many applications, from social networks to blockchain
to power grids, deep learning on non-Euclidean objects such as graphs and
manifolds, coined Geometric Deep Learning (GDL), continues to gain an ever
increasing interest. We propose a new L\'evy Flights Graph Convolutional
Networks (LFGCN) method for semi-supervised learning, which casts the L\'evy
Flights into random walks on graphs and, as a result, allows both to accurately
account for the intrinsic graph topology and to substantially improve
classification performance, especially for heterogeneous graphs. Furthermore,
we propose a new preferential P-DropEdge method based on the Girvan-Newman
argument. That is, in contrast to uniform removing of edges as in DropEdge,
following the Girvan-Newman algorithm, we detect network periphery structures
using information on edge betweenness and then remove edges according to their
betweenness centrality. Our experimental results on semi-supervised node
classification tasks demonstrate that the LFGCN coupled with P-DropEdge
accelerates the training task, increases stability and further improves
predictive accuracy of learned graph topology structure. Finally, in our case
studies we bring the machinery of LFGCN and other deep networks tools to
analysis of power grid networks - the area where the utility of GDL remains
untapped.Comment: To Appear in the 2020 IEEE International Conference on Data Mining
(ICDM
Graph Few-shot Learning via Knowledge Transfer
Towards the challenging problem of semi-supervised node classification, there
have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have
aroused great interest recently, which update the representation of each node
by aggregating information of its neighbors. However, most GNNs have shallow
layers with a limited receptive field and may not achieve satisfactory
performance especially when the number of labeled nodes is quite small. To
address this challenge, we innovatively propose a graph few-shot learning (GFL)
algorithm that incorporates prior knowledge learned from auxiliary graphs to
improve classification accuracy on the target graph. Specifically, a
transferable metric space characterized by a node embedding and a
graph-specific prototype embedding function is shared between auxiliary graphs
and the target, facilitating the transfer of structural knowledge. Extensive
experiments and ablation studies on four real-world graph datasets demonstrate
the effectiveness of our proposed model.Comment: Full paper (with Appendix) of AAAI 202
- …