Lu Bai

AAAI Conference 2026 Conference Paper

HyperAim: Hypergraph Contrastive Learning with Adaptive Multi-frequency Filters

• Ming Li
• Ruiting Zhao
• Zihao Yan
• Lu Bai
• Lixin Cui
• Feilong Cao

Unsupervised hypergraph representation learning has recently gained traction for its ability to model complex high-order interactions without requiring labeled data. However, existing contrastive learning methods typically overlook the frequency diversity inherent in hypergraph signals. To address this issue, we propose HyperAim, a contrastive learning framework that integrates adaptive multi-frequency filtering through both decoupled and coupled designs. Specifically, HyperAim employs two decoupled channels with polynomial low-pass and high-pass filters to separately capture distinct frequency components, and a third channel based on framelet decomposition that adaptively fuses multi-frequency signals in a coupled manner. A frequency-aware contrastive learning strategy is introduced to align representations across views using a combination of InfoNCE loss and pseudo-label-guided supervision. Extensive experiments across 12 benchmark datasets, covering both homophilic and heterophilic hypergraphs, demonstrate the consistent superiority of HyperAim over 17 baselines. Ablation studies further confirm the benefits of explicitly modeling and aligning frequency-specific representations.

AAAI Conference 2026 Conference Paper

HyperNoRA: Hyperedge Prediction via Node-Level Relation-Aware Self-Supervised Hypergraph Learning

• Ming Li
• Zhanle Zhu
• Xinyi Li
• Lu Bai
• Lixin Cui
• Feilong Cao
• Ke Lv

Hyperedge prediction plays a critical role in high-order relational modeling with hypergraphs, yet most existing methods primarily focus on sampling strategies or local aggregation within candidate hyperedges. These approaches often overlook global structural dependencies that are essential for learning expressive node and hyperedge representations. In this paper, we propose HyperNoRA, a novel self-supervised hypergraph learning framework that integrates global node-level relation awareness with contrastive learning. Specifically, we construct a global node relation graph that captures both direct and indirect structural correlations, which guides a structure-aware aggregator to enhance node representations with informative global context. To prevent over-smoothing and maintain discriminability, a contrastive learning module is introduced to align representations across graph augmentations while separating semantically dissimilar nodes. Extensive experiments on several benchmark datasets demonstrate that HyperNoRA consistently outperforms state-of-the-art baselines, and ablation studies verify the effectiveness of its key components.

AAAI Conference 2026 Conference Paper

LGAN: An Efficient High-Order Graph Neural Network via the Line Graph Aggregation

• Lin Du
• Lu Bai
• Jincheng Li
• Lixin Cui
• Hangyuan Du
• Lichi Zhang
• Yuting Chen
• Zhao Li

Graph Neural Networks (GNNs) have emerged as a dominant paradigm for graph classification. Specifically, most existing GNNs mainly rely on the message passing strategy between neighbor nodes, where the expressivity is limited by the 1-dimensional Weisfeiler-Lehman (1-WL) test. Although a number of k-WL-based GNNs have been proposed to overcome this limitation, their computational cost increases rapidly with k, significantly restricting the practical applicability. Moreover, since the k-WL models mainly operate on node tuples, these k-WL-based GNNs cannot retain fine-grained node- or edge-level semantics required by attribution methods (e.g., Integrated Gradients), leading to the less interpretable problem. To overcome the above shortcomings, in this paper, we propose a novel Line Graph Aggregation Network (LGAN), that constructs a line graph from the induced subgraph centered at each node to perform the higher-order aggregation. We theoretically prove that the LGAN not only possesses the greater expressive power than the 2-WL under injective aggregation assumptions, but also has lower time complexity. Empirical evaluations on benchmarks demonstrate that the LGAN outperforms state-of-the-art k-WL-based GNNs, while offering better interpretability.

AAAI Conference 2026 Conference Paper

Multi-Granular Graph Learning with Fine-Grained Behavioral Pattern Awareness for Session-Based Recommendation

• Ming Li
• Zihao Yan
• Yuting Chen
• Lixin Cui
• Lu Bai
• Feilong Cao
• Ke Lv
• Zhao Li

Session-based recommendation aims to predict users’ next actions by modeling their ongoing interaction sequences, particularly in scenarios where long-term user profiles are unavailable. While existing methods have achieved promising results by leveraging sequential and graph-based structures, they often rely on global aggregation strategies that emphasize dominant user interests while overlooking the transient and fine-grained behavior patterns embedded in sessions. In practice, user intent evolves across sessions and is reflected through diverse behavioral patterns, ranging from immediate preferences to segmented co-occurrence interests and long-range goals. To address these limitations, we propose GraphFine, a novel multi-granular graph learning framework that achieves fine-grained behavioral pattern awareness for session-based recommendation. Our approach models user behavior at different temporal and semantic granularities through a combination of graph and hypergraph neural networks. Specifically, we employ a position-aware graph to capture short-term item transitions, and construct segmented co-occurrence hypergraphs to uncover high-order semantic relations among co-occurred items. To preserve diverse user intents, we further introduce a multi-view intent readout mechanism that extracts and adaptively integrates intent signals from short-term actions, segmented co-occurrence patterns, and entire sessions. Extensive experiments on benchmark datasets demonstrate that GraphFine consistently outperforms existing state-of-the-art methods, confirming its effectiveness in capturing fine-grained and dynamic user preferences for more accurate recommendation.

AAAI Conference 2026 Conference Paper

Permutation Equivariant Framelet-based Hypergraph Neural Networks

• Ming Li
• Yi Wang
• Chengling Gao
• Lu Bai
• Yujie Fang
• Xiaosheng Zhuang
• Pietro Lio

Hypergraphs provide a natural and expressive framework for modeling high-order relationships, enabling the representation of group-wise interactions beyond pairwise connections. While hypergraph neural networks (HNNs) have shown promise for learning on such structures, existing models often rely on shallow message passing and lack the ability to extract multiscale patterns. Framelet-based techniques offer a principled solution by decomposing signals into multiple frequency bands. However, most prior framelet systems, particularly Haar-type ones, are sensitive to node ordering and fail to ensure consistent representations under permutation, leading to instability in hypergraph learning. To address this, we propose Permutation Equivariant Framelet-based Hypergraph Neural Networks (PEF-HNN), a novel framework that integrates multiscale framelet analysis with permutation-consistent learning. We construct a new family of permutation equivariant Haar-type framelets specifically designed for hypergraphs, supported by theoretical analysis of their stability and decomposition properties. Built upon these framelets, PEF-HNN incorporates both low-pass and high-pass components across multiple scales into a unified neural architecture. Extensive experiments on nine benchmark datasets, including three homophilic and four heterophilic hypergraphs, as well as two real-world datasets for visual object classification, demonstrate the effectiveness of our approach, consistently outperforming existing HNN baselines and highlighting the advantages of permutation equivariant framelet design in hypergraph representation learning.

JBHI Journal 2026 Journal Article

RT-SAM: Visual-Prompt Fusion and Uncertainty Enhancement for Nasopharyngeal Carcinoma Radiotherapy Target Delineation

• Hee Guan Khor
• Xin Yang
• Yihua Sun
• Sijuan Huang
• Yingni Wang
• Jie Wang
• Shaobin Wang
• Lu Bai

Precise delineation of the clinical target volume (CTV) and nodal CTV (CTV $_{{\mathit{nd}}}$ ) is crucial for effective radiotherapy planning in nasopharyngeal carcinoma (NPC). Manual contouring is labor-intensive and subject to substantial inter-observer variability, particularly in regions with complex anatomy and indistinct boundaries. This study presents RT-SAM, a novel framework that adapts the Medical Segment Anything Model 2 (MedSAM-2) for automated CTV (i. e. , primary CTV and CTV $_{nd}$ ) contouring in NPC computed tomography (CT) images. The framework synergistically integrates a generalist foundation model (MedSAM-2) with a domain-specific specialist network (2D U-Net) through three principal contributions: (1) automated generation of multi-modal prompts—comprising mask, bounding box, and point representations—derived from specialist network predictions to guide the generalist model; (2) a Visual-Prompt Fusion Attention (ViPFA) mechanism that optimizes feature-prompt interactions through bidirectional cross-modal attention; and (3) an Uncertainty-Enhanced Prediction Adjustment (UEPA) mechanism that enhances model robustness via confidence-based refinement and selective domain adaptation. Comprehensive evaluation on a multi-center cohort of 256 clinical NPC cases from Sun Yat-sen University Cancer Center and 212 public NPC cases from the SegRap2025 lymph node CTV dataset using 5- fold cross-validation demonstrates that RT-SAM achieves a mean DICE coefficient of 0. 796 $\pm$ 0. 033 (mean $\pm$ standard deviation), significantly outperforming current state-of-the-art methods. Clinical validation by eight radiation oncologists demonstrates that RT-SAM contours are clinically indistinguishable from expert delineations in blinded Turing assessments, achieve superior quality ratings in 75% of comparisons with mean scores of 2. 73 for RT-SAM versus 2. 66 for manual expert contours, and attain clinically acceptable ratings in over 97% of cases. These results demonstrate that RT-SAM is a clinically feasible solution for automated CTV contouring, with strong potential to standardize treatment planning and mitigate inter-observer variability in NPC radiotherapy.

AAAI Conference 2026 Conference Paper

Self-Supervised Hypergraph Learning with Substructure Awareness for Hyperedge Prediction

• Ming Li
• Huiting Wang
• Yuting Chen
• Lu Bai
• Lixin Cui
• Feilong Cao
• Ke Lv

Hyperedge prediction plays a central role in hypergraph learning, enabling the inference of high-order relations among multiple entities. However, existing methods often rely on a simplistic flat set assumption, treating candidate hyperedges as unstructured collections of nodes and neglecting their potential internal compositionality. Furthermore, the severe scarcity of observed hyperedges poses a challenge for effective supervision. In this work, we propose S3Hyper, a Substructure-contextualized Self-Supervised framework for Hyperedge prediction, which jointly addresses these two challenges. Specifically, we design a substructure-contextualized hyperedge aggregator that models the internal hierarchy of candidate hyperedges by leveraging sub-hyperedge information. In parallel, we introduce an adaptive tri-directional contrastive learning module that incorporates node-level, hyperedge-level, and cross-level alignment objectives, supported by temperature-adaptive mechanisms. Experimental results on four public datasets demonstrate that S3Hyper consistently outperforms strong baselines, with ablation studies verifying the effectiveness of each component.

AAAI Conference 2026 Conference Paper

SSHPool: The Separated Subgraph-based Hierarchical Pooling

• Zhuo Xu
• Lu Bai
• Lixin Cui
• Ming Li
• Hangyuan Du
• Ziyu Lyu
• Yue Wang
• Edwin R. Hancock

In this paper, we develop a novel local graph pooling method, namely the Separated Subgraph-based Hierarchical Pooling (SSHPool), for graph classification. We commence by assigning the nodes of a sample graph into different clusters, resulting in a family of separated subgraphs. We individually employ the local graph convolution units as the local structure to further compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. Since these subgraphs are separated by different clusters and the structural information cannot be propagated between them, the local convolution operation can significantly avoid the over-smoothing problem caused by message passing through edges in most existing Graph Neural Networks (GNNs). By hierarchically performing the proposed procedures on the resulting coarsened graph, the proposed SSHPool can effectively extract the hierarchical global features of the original graph structure, encapsulating rich intrinsic structural characteristics. Furthermore, we develop an end-to-end GNN framework associated with the SSHPool module for graph classification. Experimental results demonstrate the superior performance of the proposed model on real-world datasets.

IJCAI Conference 2025 Conference Paper

AKBR: Learning Adaptive Kernel-based Representations for Graph Classification

• Lu Bai
• Feifei Qian
• Lixin Cui
• Ming Li
• Hangyuan Du
• Yue Wang
• Edwin Hancock

In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are defined by merely counting any pair of isomorphic substructures between graphs and cannot provide an end-to-end learning mechanism for the classifier, the proposed AKBR approach aims to define an end-to-end representation learning model to construct an adaptive kernel matrix for graphs. To this end, we commence by leveraging a novel feature-channel attention mechanism to capture the interdependencies between different substructure invariants of original graphs. The proposed AKBR model can thus effectively identify the structural importance of different substructures, and compute the R-convolution kernel between pairwise graphs associated with the more significant substructures specified by their structural attentions. Furthermore, the proposed AKBR model employs all sample graphs as the prototype graphs, naturally providing an end-to-end learning architecture between the kernel computation as well as the classifier. Experimental results show that the proposed AKBR model outperforms existing state-of-the-art graph kernels and deep learning methods on standard graph benchmarks.

IJCAI Conference 2025 Conference Paper

An End-to-End Simple Clustering Hierarchical Pooling Operation for Graph Learning Based on Top-K Node Selection

• Zhehan Zhao
• Lu Bai
• Ming Li
• Lixin Cui
• Hangyuan Du
• Yue Wang
• Edwin Hancock

Graph Neural Networks (GNNs) are powerful tools for graph learning, but one of the important challenges is how to effectively extract representations for graph-level tasks. In this paper, we propose an end-to-end Simple Clustering Hierarchical Pooling (SCHPool) operation, which is based on Top-K node selection for learning expressive graph representations. Specifically, SCHPool considers each node and its local neighborhood as a cluster, and introduces a novel multi-view scoring function to evaluate node importance. Based on these scores, clusters centered around the Top-K nodes are retained. This design eliminates the need for complex clustering operations, significantly reducing computational overhead. Furthermore, during the coarsening process, SCHPool employs a lightweight yet comprehensive attention mechanism to adaptively aggregate both the node features within clusters and the edge connectivity strengths between clusters. This facilitates the construction of more informative coarsened graphs, enhancing model performance. Experimental results demonstrate the effectiveness of the proposed model.

AAAI Conference 2025 Conference Paper

Deep Hypergraph Neural Networks with Tight Framelets

• Ming Li
• Yujie Fang
• Yi Wang
• Han Feng
• Yongchun Gu
• Lu Bai
• Pietro Liò

Hypergraphs provide a flexible framework for modeling high-order (complex) interactions among multiple entities, extending beyond traditional pairwise correlations in graph structures. However, deep hypergraph neural networks (HGNNs) often face the challenge of oversmoothing with increasing depth, similar to issues in graph neural networks (GNNs). While oversmoothing in GNNs has been extensively studied, its implications in relation to hypergraphs are less explored. This paper addresses this gap by first theoretically exploring the reasons behind oversmoothing in deep HGNNs. Our novel insights suggest that a spectral-based hypergraph convolution, equipped with both low-pass and high-pass filters, can potentially mitigate these effects. Motivated by these findings, we introduce FrameHGNN, a framework that utilizes framelet-based hypergraph convolutions integrating tight framelet transforms with both low-pass and high-pass components, as well as the commonly used strategies in designing deep GNN architecture: initial residual and identity mappings. The experiment results on diverse benchmark datasets demonstrate that FrameHGNN outperforms several state-of-the-art models, effectively reducing oversmoothing while improving predictive accuracy. Our contributions not only advance the theoretical understanding of deep hypergraph learning but also provide a practical spectral-based approach for HGNNs, emphasizing the design of multifrequency channels.

AAAI Conference 2025 Conference Paper

DHAKR: Learning Deep Hierarchical Attention-Based Kernelized Representations for Graph Classification

• Feifei Qian
• Lu Bai
• Lixin Cui
• Ming Li
• Ziyu Lyu
• Hangyuan Du
• Edwin Hancock

Graph-based representations are powerful tools for analyzing structured data. In this paper, we propose a novel model to learn Deep Hierarchical Attention-based Kernelized Representations (DHAKR) for graph classification. To this end, we commence by learning an assignment matrix to hierarchically map the substructure invariants into a set of composite invariants, resulting in hierarchical kernelized representations for graphs. Moreover, we introduce the feature-channel attention mechanism to capture the interdependencies between different substructure invariants that will be converged into the composite invariants, addressing the shortcoming of discarding the importance of different substructures arising in most existing R-convolution graph kernels. We show that the proposed DHAKR model can adaptively compute the kernel-based similarity between graphs, identifying the common structural patterns over all graphs. Experiments demonstrate the effectiveness of the proposed DHAKR model.

IJCAI Conference 2025 Conference Paper

DHTAGK: Deep Hierarchical Transitive-Aligned Graph Kernels for Graph Classification

• Xinya Qin
• Lu Bai
• Lixin Cui
• Ming Li
• Ziyu Lyu
• Hangyuan Du
• Edwin Hancock

In this paper, we propose a family of novel Deep Hierarchical Transitive-Aligned Graph Kernels (DHTAGK) for graph classification. To this end, we commence by developing a new Hierarchical Aligned Graph Auto-Encoder (HA-GAE) to construct transitive-aligned embedding graphs that encapsulate the structural correspondence information between graphs. The DHTAGK kernels then measure either the Jensen-Shannon Divergence between the adjacency matrices or the Gaussian kernel between the node feature matrices of the embedding graphs. Unlike the classical R-convolution kernels and node-based alignment kernels, the DHTAGK kernels can capture the transitive structural correspondence information and thus ensure the positive definiteness. Furthermore, the HA-GAE enables the DHTAGK kernels to simultaneously reflect both local and global graph structures and identify common structural patterns. Experimental results show that the DHTAGK kernels outperform state-of-the-art graph kernels and deep learning methods on benchmark datasets.

IJCAI Conference 2025 Conference Paper

Exploring the Over-smoothing Problem of Graph Neural Networks for Graph Classification: An Entropy-based Viewpoint

• Feifei Qian
• Lu Bai
• Lixin Cui
• Ming Li
• Hangyuan Du
• Yue Wang
• Edwin Hancock

The over-smoothing has emerged as a major challenge in the development of Graph Neural Networks (GNNs). While existing state-of-the-art methods effectively mitigate the diminishing distance between nodes and improve the performance of node classification, they tend to be elusive for graph-level tasks. This paper introduces a novel entropy-based perspective to explore the over-smoothing problem, simultaneously enhancing the distinguishability of non-isomorphic graphs. We provide a theoretical analysis of the relationship between the smoothness and the entropy for graphs, highlighting how the over-smoothing in high-entropic regions negatively impact the graph classification performance. To tackle this issue, we propose a simple yet effective method to Sample and Discretize node features in high-Entropic regions (SDE), aiming to preserve the critical and complicated structural information. Moreover, we introduce a new evaluation metric to assess the over-smoothing for graph-level tasks, focusing on node distributions. Experimental results demonstrate that the proposed SDE method significantly outperforms existing state-of-the-art methods, establishing a new benchmark in the field of GNNs.

IJCAI Conference 2025 Conference Paper

HA-SCN: Learning Hierarchical Aligned Subtree Convolutional Networks for Graph Classification

• Xinya Qin
• Lu Bai
• Lixin Cui
• Ming Li
• Hangyuan Du
• Yue Wang
• Edwin Hancock

In this paper, we propose a Hierarchical Aligned Subtree Convolutional Network (HA-SCN) for graph classification. Our idea is to transform graphs of arbitrary sizes into fixed-sized aligned graphs and construct a normalized K-layer m-ary subtree for each node in the aligned graphs. By sliding convolutional filters over the entire subtree at each node, we define a novel subtree convolution and pooling operation that hierarchically abstracts node-level information. We demonstrate that the proposed HA-SCN model not only realizes the convolution mechanism similar to the Convolutional Neural Networks (CNNs), which have the characteristics of weight sharing and fixed-sized receptive fields, but also effectively mitigates the over-squashing problem. Meanwhile, it establishes the correspondence information between nodes, alleviating the information loss issue. Experimental results on various benchmark graph datasets show that our approach achieves state-of-the-art performance in graph classification tasks.

IJCAI Conference 2025 Conference Paper

MATCH: Modality-Calibrated Hypergraph Fusion Network for Conversational Emotion Recognition

• Jiandong Shi
• Ming Li
• Lu Bai
• Feilong Cao
• Ke Lu
• Jiye Liang

Multimodal emotion recognition aims to identify emotions by integrating multimodal features derived from spoken utterances. However, existing work often neglects the calibration of conversational entities, focusing mainly on extracting potential intra- or cross-modal information. This leads to the underutilization of utterance information that is essential for accurately characterizing emotion. Additionally, the lack of effective modeling of conversational patterns limits the ability to capture emotional pathways across contexts, modalities and speakers, impacting the overall emotional understanding. In this study, we propose the modality-calibrated hypergraph fusion network (MATCH), which leverages multimodal fusion and hypergraph learning techniques to address these challenges. In particular, we introduce an entity calibration strategy that refines the representations of conversational entities both at the modality and context levels, allowing for deeper insights into emotion-related cues. Furthermore, we present an emotion-aligned hypergraph fusion method that incorporates a line graph to explore conversational patterns, facilitating flexible knowledge transfer across modalities through hyperedge-level and graph-level alignments. Experiments demonstrate that MATCH outperforms state-of-the-art approaches on two benchmark datasets.

NeurIPS Conference 2025 Conference Paper

MultiNet: Adaptive Multi-Viewed Subgraph Convolutional Networks for Graph Classification

• Xinya Qin
• Lu Bai
• Lixin Cui
• Ming Li
• Hangyuan Du
• Edwin Hancock

The problem of over-smoothing has emerged as a fundamental issue for Graph Convolutional Networks (GCNs). While existing efforts primarily focus on enhancing the discriminability of node representations for node classification, they tend to overlook the over-smoothing at the graph level, significantly influencing the performance of graph classification. In this paper, we provide an explanation of the graph-level over-smoothing phenomenon and propose a novel Adaptive Multi-Viewed Subgraph Convolutional Network (MultiNet) to address this challenge. Specifically, the MultiNet introduces a local subgraph convolution module that adaptively divides each input graph into multiple subgraph views. Then a number of subgraph-based view-specific convolution operations are applied to constrain the extent of node information propagation over the original global graph structure, not only mitigating the over-smoothing issue but also generating more discriminative local node representations. Moreover, we develop an alignment-based readout that establishes correspondences between nodes over different graphs, thereby effectively preserving the local node-level structure information and improving the discriminative ability of the resulting graph-level representations. Theoretical analysis and empirical studies show that the MultiNet mitigates the graph-level over-smoothing and achieves excellent performance for graph classification.

IJCAI Conference 2025 Conference Paper

SpaceDet: A Large-scale Space-based Image Dataset and RSO Detection for Space Situational Awareness

• Jiaping Xiao
• Rangya Zhang
• Yuhang Zhang
• Lu Bai
• Qianlei Jia
• Mir Feroskhan

Space situational awareness (SSA) plays an imperative role in maintaining safe space operations, especially given the increasingly congested space traffic around the Earth. Space-based SSA offers a flexible and lightweight solution compared to traditional ground-based SSA. With advanced machine learning approaches, space-based SSA can extract features from high-resolution images in space to detect and track resident space objects (RSOs). However, existing spacecraft image datasets, such as SPARK, fall short of providing realistic camera observations, rendering the derived algorithms unsuitable for real SSA systems. In this work, we introduce SpaceDet, a large-scale realistic space-based image dataset for SSA. We consider accurate space orbit dynamics and a physical camera model with various noise distributions, generating images at the photon level. To extend the available observation window, four overlapping cameras are simulated with a fixed rotation angle. SpaceDet includes images of RSOs observed from 19 km to 63, 000 km, captured by a tracker operating in LEO, MEO, and GEO orbits over a period of 5, 000 seconds. Each image has a resolution of 4418 x 4418 pixels, providing detailed features for developing advanced SSA approaches. We split the dataset into three subsets: SpaceDet-100, SpaceDet-5000, and SpaceDet-full, catering to various image processing applications. The SpaceDet-full corpus includes a comprehensive dataloader with 781. 5 GB of images and 25. 9 MB of ground truth labels. Furthermore, we adapted detection and tracking algorithms on the collected dataset using a specified splitting method to accelerate the training process. The trained model can detect RSOs from real-world space observations with zero-shot capability.

AAAI Conference 2025 Conference Paper

When Hypergraph Meets Heterophily: New Benchmark Datasets and Baseline

• Ming Li
• Yongchun Gu
• Yi Wang
• Yujie Fang
• Lu Bai
• Xiaosheng Zhuang
• Pietro Liò

Hypergraph neural networks (HNNs) have shown promise in handling tasks characterized by high-order correlations, achieving notable success across various applications. However, there has been limited focus on heterophilic hypergraph learning (HHL), in contrast to the increasing attention given to graph neural networks designed for graphs exhibiting heterophily. This paper aims to pave the way for HHL by addressing key gaps from multiple perspectives: measurement, dataset diversity, and baseline model development. First, we introduce metrics to quantify heterophily in hypergraphs, providing a numerical basis for assessing the homophily/heterophily ratio. Second, we develop diverse benchmark datasets across various real-world scenarios, facilitating comprehensive evaluations of existing HNNs and advancing research in HHL. Additionally, as a novel baseline model, we propose HyperUFG, a framelet-based HNN integrating both low-pass and high-pass filters. Extensive experiments conducted on synthetic and benchmark datasets highlight the challenges current HNNs face with heterophilic hypergraphs, while showcasing that HyperUFG performs competitively and often outperforms many existing models in such scenarios. Overall, our study underscores the urgent need for further exploration and development in this emerging field, with the potential to inspire and guide future research in HHL.

NeurIPS Conference 2024 Conference Paper

HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning

• Lu Bai
• Zhuo Xu
• Lixin Cui
• Ming Li
• Yue Wang
• Edwin Hancock

Graph Auto-Encoders (GAEs) are powerful tools for graph representation learning. In this paper, we develop a novel Hierarchical Cluster-based GAE (HC-GAE), that can learn effective structural characteristics for graph data analysis. To this end, during the encoding process, we commence by utilizing the hard node assignment to decompose a sample graph into a family of separated subgraphs. We compress each subgraph into a coarsened node, transforming the original graph into a coarsened graph. On the other hand, during the decoding process, we adopt the soft node assignment to reconstruct the original graph structure by expanding the coarsened nodes. By hierarchically performing the above compressing procedure during the decoding process as well as the expanding procedure during the decoding process, the proposed HC-GAE can effectively extract bidirectionally hierarchical structural features of the original sample graph. Furthermore, we re-design the loss function that can integrate the information from either the encoder or the decoder. Since the associated graph convolution operation of the proposed HC-GAE is restricted in each individual separated subgraph and cannot propagate the node information between different subgraphs, the proposed HC-GAE can significantly reduce the over-smoothing problem arising in the classical convolution-based GAEs. The proposed HC-GAE can generate effective representations for either node classification or graph classification, and the experiments demonstrate the effectiveness on real-world datasets.

EAAI Journal 2023 Journal Article

Pre-clustering active learning method for automatic classification of building structures in urban areas

• Peng Zhou
• Tongxin Zhang
• Liwen Zhao
• Yifan Qi
• Yuan Chang
• Lu Bai

IJCAI Conference 2020 Conference Paper

A Quantum-inspired Entropic Kernel for Multiple Financial Time Series Analysis

• Lu Bai
• Lixin Cui
• Yue Wang
• Yuhang Jiao
• Edwin R. Hancock

Network representations are powerful tools for the analysis of time-varying financial complex systems consisting of multiple co-evolving financial time series, e. g. , stock prices, etc. In this work, we develop a new kernel-based similarity measure between dynamic time-varying financial networks. Our ideas is to transform each original financial network into quantum-based entropy time series and compute the similarity measure based on the classical dynamic time warping framework associated with the entropy time series. The proposed method bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks abstracted from financial time series of New York Stock Exchange (NYSE) database demonstrate that our approach can effectively discriminate the abrupt structural changes in terms of the extreme financial events.

IJCAI Conference 2015 Conference Paper

A Graph Kernel Based on the Jensen-Shannon Representation Alignment

• Lu Bai
• Zhihong Zhang
• Chaoyan Wang
• Xiao Bai
• Edwin Hancock

In this paper, we develop a novel graph kernel by aligning the Jensen-Shannon (JS) representations of vertices. We commence by describing how to compute the JS representation of a vertex by measuring the JS divergence (JSD) between the corresponding h-layer depth-based (DB) representations developed in [Bai et al. , 2014a]). By aligning JS representations of vertices, we identify the correspondence between the vertices of two graphs and this allows us to construct a matching-based graph kernel. Unlike existing R-convolution kernels [Haussler, 1999] that roughly record the isomorphism information between any pair of substructures under a type of graph decomposition, the new kernel can be seen as an aligned subgraph kernel that incorporates explicit local correspondences of substructures (i. e. , the local information graphs [Dehmer and Mowshowitz, 2011]) into the process of kernelization through the JS representation alignment. The new kernel thus addresses the drawback of neglecting the relative locations between substructures that arises in the R-convolution kernels. Experiments demonstrate that our kernel can easily outperform state-of-the-art graph kernels in terms of the classification accuracies.