16 research outputs found
Hypergraph Neural Networks
In this paper, we present a hypergraph neural networks (HGNN) framework for
data representation learning, which can encode high-order data correlation in a
hypergraph structure. Confronting the challenges of learning representation for
complex data in real practice, we propose to incorporate such data structure in
a hypergraph, which is more flexible on data modeling, especially when dealing
with complex data. In this method, a hyperedge convolution operation is
designed to handle the data correlation during representation learning. In this
way, traditional hypergraph learning procedure can be conducted using hyperedge
convolution operations efficiently. HGNN is able to learn the hidden layer
representation considering the high-order data structure, which is a general
framework considering the complex data correlations. We have conducted
experiments on citation network classification and visual object recognition
tasks and compared HGNN with graph convolutional networks and other traditional
methods. Experimental results demonstrate that the proposed HGNN method
outperforms recent state-of-the-art methods. We can also reveal from the
results that the proposed HGNN is superior when dealing with multi-modal data
compared with existing methods.Comment: Accepted in AAAI'201
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction
Along with the proliferation of electric vehicles (EVs), optimizing the use
of EV charging space can significantly alleviate the growing load on
intelligent transportation systems. As the foundation to achieve such an
optimization, a spatiotemporal method for EV charging demand prediction in
urban areas is required. Although several solutions have been proposed by using
data-driven deep learning methods, it can be found that these
performance-oriented methods may suffer from misinterpretations to correctly
handle the reverse relationship between charging demands and prices. To tackle
the emerging challenges of training an accurate and interpretable prediction
model, this paper proposes a novel approach that enables the integration of
graph and temporal attention mechanisms for feature extraction and the usage of
physic-informed meta-learning in the model pre-training step for knowledge
transfer. Evaluation results on a dataset of 18,013 EV charging piles in
Shenzhen, China, show that the proposed approach, named PAG, can achieve
state-of-the-art forecasting performance and the ability in understanding the
adaptive changes in charging demands caused by price fluctuations.Comment: Preprint. This work has been submitted to the IEEE Transactions on
ITS for possible publication. Copyright may be transferred without notice,
after which this version may no longer be accessibl
MeshNet: Mesh Neural Network for 3D Shape Representation
Mesh is an important and powerful type of data for 3D shapes and widely
studied in the field of computer vision and computer graphics. Regarding the
task of 3D shape representation, there have been extensive research efforts
concentrating on how to represent 3D shapes well using volumetric grid,
multi-view and point cloud. However, there is little effort on using mesh data
in recent years, due to the complexity and irregularity of mesh data. In this
paper, we propose a mesh neural network, named MeshNet, to learn 3D shape
representation from mesh data. In this method, face-unit and feature splitting
are introduced, and a general architecture with available and effective blocks
are proposed. In this way, MeshNet is able to solve the complexity and
irregularity problem of mesh and conduct 3D shape representation well. We have
applied the proposed MeshNet method in the applications of 3D shape
classification and retrieval. Experimental results and comparisons with the
state-of-the-art methods demonstrate that the proposed MeshNet can achieve
satisfying 3D shape classification and retrieval performance, which indicates
the effectiveness of the proposed method on 3D shape representation