Deep Hypergraph Structure Learning

by   Zizhao Zhang, et al.

Learning on high-order correlation has shown superiority in data representation learning, where hypergraph has been widely used in recent decades. The performance of hypergraph-based representation learning methods, such as hypergraph neural networks, highly depends on the quality of the hypergraph structure. How to generate the hypergraph structure among data is still a challenging task. Missing and noisy data may lead to "bad connections" in the hypergraph structure and destroy the hypergraph-based representation learning process. Therefore, revealing the high-order structure, i.e., the hypergraph behind the observed data, becomes an urgent but important task. To address this issue, we design a general paradigm of deep hypergraph structure learning, namely DeepHGSL, to optimize the hypergraph structure for hypergraph-based representation learning. Concretely, inspired by the information bottleneck principle for the robustness issue, we first extend it to the hypergraph case, named by the hypergraph information bottleneck (HIB) principle. Then, we apply this principle to guide the hypergraph structure learning, where the HIB is introduced to construct the loss function to minimize the noisy information in the hypergraph structure. The hypergraph structure can be optimized and this process can be regarded as enhancing the correct connections and weakening the wrong connections in the training phase. Therefore, the proposed method benefits to extract more robust representations even on a heavily noisy structure. Finally, we evaluate the model on four benchmark datasets for representation learning. The experimental results on both graph- and hypergraph-structured data demonstrate the effectiveness and robustness of our method compared with other state-of-the-art methods.


page 1

page 2

page 3

page 4


Hypergraph Neural Networks

In this paper, we present a hypergraph neural networks (HGNN) framework ...

Binary Classification in Unstructured Space With Hypergraph Case-Based Reasoning

Binary classification is one of the most common problem in machine learn...

Preventing Over-Smoothing for Hypergraph Neural Networks

In recent years, hypergraph learning has attracted great attention due t...

HNHN: Hypergraph Networks with Hyperedge Neurons

Hypergraphs provide a natural representation for many real world dataset...

Scalable Hypergraph Visualization

Hypergraph visualization has many applications in network data analysis....

HGIB: Prognosis for Alzheimer's Disease via Hypergraph Information Bottleneck

Alzheimer's disease prognosis is critical for early Mild Cognitive Impai...

Road Network Representation Learning: A Dual Graph based Approach

Road network is a critical infrastructure powering many applications inc...

Please sign up or login with your details

Forgot password? Click here to reset