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Junbo Ma

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6 papers

1 author row

AAAI Conference 2024 Conference Paper

Multiscale Attention Wavelet Neural Operator for Capturing Steep Trajectories in Biochemical Systems

Jiayang Su
Junbo Ma
Songyang Tong
Enze Xu
Minghan Chen

In biochemical modeling, some foundational systems can exhibit sudden and profound behavioral shifts, such as the cellular signaling pathway models, in which the physiological responses promptly react to environmental changes, resulting in steep changes in their dynamic model trajectories. These steep changes are one of the major challenges in biochemical modeling governed by nonlinear differential equations. One promising way to tackle this challenge is converting the input data from the time domain to the frequency domain through Fourier Neural Operators, which enhances the ability to analyze data periodicity and regularity. However, the effectiveness of these Fourier based methods diminishes in scenarios with complex abrupt switches. To address this limitation, an innovative Multiscale Attention Wavelet Neural Operator (MAWNO) method is proposed in this paper, which comprehensively combines the attention mechanism with the versatile wavelet transforms to effectively capture these abrupt switches. Specifically, the wavelet transform scrutinizes data across multiple scales to extract the characteristics of abrupt signals into wavelet coefficients, while the self-attention mechanism is adeptly introduced to enhance the wavelet coefficients in high-frequency signals that can better characterize the abrupt switches. Experimental results substantiate MAWNO’s supremacy in terms of accuracy on three classical biochemical models featuring periodic and steep trajectories. https://github.com/SUDERS/MAWNO.

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IJCAI Conference 2024 Conference Paper

Self-Promoted Clustering-based Contrastive Learning for Brain Networks Pretraining

Junbo Ma
Caixuan Luo
Jia Hou
Kai Zhao

Rapid advancements in neuroimaging techniques, such as magnetic resonance imaging (MRI), have facilitated the acquisition of the structural and functional characteristics of the brain. Brain network analysis is one of the essential tools for exploring brain mechanisms from MRI, providing valuable insights into the brain's organization, and stimulating the understanding of brain cognition and pathology of neurodegenerative diseases. Graph Neural Networks (GNNs) are commonly used for brain network analysis, but they are limited by the scarcity of medical data. Although Graph Contrastive Learning methods have been developed to address this, they often involve graph augmentations that distort the anatomical brain structures. To address these challenges, an augmentation-free contrastive learning method, named Self-Promoted Clustering-based Contrastive Learning(SPCCL), is proposed in this paper. Specifically, by introducing a clustering-based contrastive Learning loss and a self-promoted contrastive pairs creation scheme, the proposed SPCCL can be pre-trained from additional healthy subjects' data that are relatively easier to acquire than disorder ones. The proposed SPCCL leverages these additional data with respect to the integrity of the original brain structure, making it a promising approach for effective brain network analysis. Comprehensive experiments are conducted on an open-access schizophrenic dataset, demonstrating the effectiveness of the proposed method.

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IJCAI Conference 2024 Conference Paper

Simple Contrastive Multi-View Clustering with Data-Level Fusion

Caixuan Luo
Jie Xu
Yazhou Ren
Junbo Ma
Xiaofeng Zhu

Previous deep multi-view clustering methods usually design un-shared encoders to explore the cluster information among multi-view data, but they are difficult to customize the encoders for individual views and easily increase information loss. To address these issues, we propose a simple yet effective contrastive multi-view clustering framework. Specifically, different from using feature-level fusion in previous methods, we first propose a data-level fusion method to fuse multi-view information, which produces a fused data to replace all views and thus avoids customizing networks for different views. Then, we simulate the data noise and unavailability in multiple views to design two kinds of data augmentation for the fused data, making a shared encoder with simple contrastive learning to learn robust features and achieve the interaction across views. As a result, our method is a general framework and we base on it to conduct feature clustering and end-to-end clustering. Extensive experiments demonstrate that our method can explore the discriminative information in multi-view data and achieve superior clustering performance.

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IJCAI Conference 2023 Conference Paper

Totally Dynamic Hypergraph Neural Networks

Peng Zhou
Zongqian Wu
Xiangxiang Zeng
Guoqiu Wen
Junbo Ma
Xiaofeng Zhu

Recent dynamic hypergraph neural networks (DHGNNs) are designed to adaptively optimize the hypergraph structure to avoid the dependence on the initial hypergraph structure, thus capturing more hidden information for representation learning. However, most existing DHGNNs cannot adjust the hyperedge number and thus fail to fully explore the underlying hypergraph structure. This paper proposes a new method, namely, totally hypergraph neural network (TDHNN), to adjust the hyperedge number for optimizing the hypergraph structure. Specifically, the proposed method first captures hyperedge feature distribution to obtain dynamical hyperedge features rather than fixed ones, by conducting the sampling from the learned distribution. The hypergraph is then constructed based on the attention coefficients of both sampled hyperedges and nodes. The node features are dynamically updated by designing a simple hypergraph convolution algorithm. Experimental results on real datasets demonstrate the effectiveness of the proposed method, compared to SOTA methods. The source code can be accessed via https: //github. com/HHW-zhou/TDHNN.

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IJCAI Conference 2022 Conference Paper

Information Augmentation for Few-shot Node Classification

Zongqian Wu
Peng Zhou
Guoqiu Wen
Yingying Wan
Junbo Ma
Debo Cheng
Xiaofeng Zhu

Although meta-learning and metric learning have been widely applied for few-shot node classification (FSNC), some limitations still need to be addressed, such as expensive time costs for the meta-train and difficult of exploring the complex structure inherent the graph data. To address in issues, this paper proposes a new data augmentation method to conduct FSNC on the graph data including parameter initialization and parameter fine-tuning. Specifically, parameter initialization only conducts a multi-classification task on the base classes, resulting in good generalization ability and less time cost. Parameter fine-tuning designs two data augmentation methods (i. e. , support augmentation and shot augmentation) on the novel classes to generate sufficient node features so that any traditional supervised classifiers can be used to classify the query set. As a result, the proposed method is the first work of data augmentation for FSNC. Experiment results show the effectiveness and the efficiency of our proposed method, compared to state-of-the-art methods, in terms of different classification tasks.

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IJCAI Conference 2020 Conference Paper

Multi-graph Fusion for Functional Neuroimaging Biomarker Detection

Jiangzhang Gan
Xiaofeng Zhu
Rongyao Hu
Yonghua Zhu
Junbo Ma
Ziwen Peng
Guorong Wu

Brain functional connectivity analysis on fMRI data could improve the understanding of human brain function. However, due to the influence of the inter-subject variability and the heterogeneity across subjects, previous methods of functional connectivity analysis are often insufficient in capturing disease-related representation so that decreasing disease diagnosis performance. In this paper, we first propose a new multi-graph fusion framework to fine-tune the original representation derived from Pearson correlation analysis, and then employ L1-SVM on fine-tuned representations to conduct joint brain region selection and disease diagnosis for avoiding the issue of the curse of dimensionality on high-dimensional data. The multi-graph fusion framework automatically learns the connectivity number for every node (i. e. , brain region) and integrates all subjects in a unified framework to output homogenous and discriminative representations of all subjects. Experimental results on two real data sets, i. e. , fronto-temporal dementia (FTD) and obsessive-compulsive disorder (OCD), verified the effectiveness of our proposed framework, compared to state-of-the-art methods.

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