861 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
Hypergraph Learning with Line Expansion
Previous hypergraph expansions are solely carried out on either vertex level
or hyperedge level, thereby missing the symmetric nature of data co-occurrence,
and resulting in information loss. To address the problem, this paper treats
vertices and hyperedges equally and proposes a new hypergraph formulation named
the \emph{line expansion (LE)} for hypergraphs learning. The new expansion
bijectively induces a homogeneous structure from the hypergraph by treating
vertex-hyperedge pairs as "line nodes". By reducing the hypergraph to a simple
graph, the proposed \emph{line expansion} makes existing graph learning
algorithms compatible with the higher-order structure and has been proven as a
unifying framework for various hypergraph expansions. We evaluate the proposed
line expansion on five hypergraph datasets, the results show that our method
beats SOTA baselines by a significant margin
Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations
Point cloud segmentation with scene-level annotations is a promising but
challenging task. Currently, the most popular way is to employ the class
activation map (CAM) to locate discriminative regions and then generate
point-level pseudo labels from scene-level annotations. However, these methods
always suffer from the point imbalance among categories, as well as the sparse
and incomplete supervision from CAM. In this paper, we propose a novel weighted
hypergraph convolutional network-based method, called WHCN, to confront the
challenges of learning point-wise labels from scene-level annotations. Firstly,
in order to simultaneously overcome the point imbalance among different
categories and reduce the model complexity, superpoints of a training point
cloud are generated by exploiting the geometrically homogeneous partition.
Then, a hypergraph is constructed based on the high-confidence superpoint-level
seeds which are converted from scene-level annotations. Secondly, the WHCN
takes the hypergraph as input and learns to predict high-precision point-level
pseudo labels by label propagation. Besides the backbone network consisting of
spectral hypergraph convolution blocks, a hyperedge attention module is learned
to adjust the weights of hyperedges in the WHCN. Finally, a segmentation
network is trained by these pseudo point cloud labels. We comprehensively
conduct experiments on the ScanNet and S3DIS segmentation datasets.
Experimental results demonstrate that the proposed WHCN is effective to predict
the point labels with scene annotations, and yields state-of-the-art results in
the community. The source code is available at
http://zhiyongsu.github.io/Project/WHCN.html
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