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Shenghui Zhang

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5 papers

1 author row

AAAI Conference 2026 Conference Paper

Connectivity-Guided Sparsification of 2-FWL GNNs: Preserving Full Expressivity with Improved Efficiency

Rongqin Chen
Fan Mo
Pak Lon Ip
Shenghui Zhang
Dan Wu
Ye Li
Leong Hou U

Higher-order Graph Neural Networks (HOGNNs) based on the 2-FWL test achieve superior expressivity by modeling 2-node and 3-node interactions, but incur cubic computational cost. Existing efficiency methods typically reduce this burden at the expense of expressivity. We propose Co-Sparsify, a connectivity-aware sparsification framework that eliminates provably redundant computations while preserving full 2-FWL expressive power. Our key insight is that 3-node interactions are expressively necessary only within biconnected components, namely, maximal subgraphs where every node pair lies on a cycle. Outside these components, structural relationships are fully captured via 2-node message passing and graph readouts, rendering higher-order modeling unnecessary. Co-Sparsify restricts 2-node message passing to connected components and 3-node interactions to biconnected components, eliminating redundant computation without approximation or sampling. We prove that Co-Sparsified GNNs match the expressivity of the 2-FWL test. Empirically, when applied to PPGN, Co-Sparsify matches or exceeds accuracy on synthetic substructure counting tasks and achieves state-of-the-art performance on real-world benchmarks (ZINC, QM9 and TUD). This study demonstrates that high expressivity and scalability are not mutually exclusive: principled, topology-guided sparsification enables powerful, efficient GNNs with theoretical guarantees.

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AAAI Conference 2025 Conference Paper

Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification

Zijie Zhou
Zhaoqi Lu
Xuekai Wei
Rongqin Chen
Shenghui Zhang
Pak Lon Ip
Leong Hou U

Graph Neural Networks (GNNs) are widely used in graph data mining tasks. Traditional GNNs follow a message passing scheme that can effectively utilize local and structural information. However, the phenomena of over-smoothing and over-squashing limit the receptive field in message passing processes. Graph Transformers were introduced to address these issues, achieving a global receptive field but suffering from the noise of irrelevant nodes and loss of structural information. Therefore, drawing inspiration from fine-grained token-based representation learning in Natural Language Processing (NLP), we propose the Structure-aware Multi-token Graph Transformer (Tokenphormer), which generates multiple tokens to effectively capture local and structural information and explore global information at different levels of granularity. Specifically, we first introduce the walk-token generated by mixed walks consisting of four walk types to explore the graph and capture structure and contextual information flexibly. To ensure local and global information coverage, we also introduce the SGPM-token (obtained through the Self-supervised Graph Pre-train Model, SGPM) and the hop-token, extending the length and density limit of the walk-token, respectively. Finally, these expressive tokens are fed into the Transformer model to learn node representations collaboratively. Experimental results demonstrate that the capability of the proposed Tokenphormer can achieve state-of-the-art performance on node classification tasks.

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EAAI Journal 2024 Journal Article

A block-based heuristic search algorithm for the two-dimensional guillotine strip packing problem

Hao Zhang
Shaowen Yao
Shenghui Zhang
Jiewu Leng
Lijun Wei
Qiang Liu

Introduction: This paper addresses the two-dimensional strip-packing (2DSP) problem of placing a set of rectangular pieces onto a fixed-width rectangular sheet to minimize the total length used. Methodology: We propose a Block-Based Heuristic Search Algorithm (BBHSA) to solve 2DSP problems with guillotine cut constraints. Initially, it converts the 2DSP problem into a series of 2D rectangular packing problems (2DRP), where the size of the sheet is fixed. In the BBHSA, rectangular pieces are aggregated into blocks, which are partial solutions without residual space. These blocks provide the ingredients for a good layout and are basic components in a tree-based constructive search process. Two basic operations, called placing & splitting and approximate binary search are used in the search process. Furthermore, several block-based placement rules are explored to speed up the search process and improve solution quality. Result discussion: To verify the performance of our proposed algorithm, we conducted extensive experiments using the zero-waste benchmark and non-zero-waste benchmark instances. Final results: The results show that BBHSA demonstrates computational effectiveness, particularly in zero-waste cases, achieving optimal solutions for almost all zero-waste benchmark instances reported in the existing literature.

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AAAI Conference 2024 Conference Paper

A Computation-Aware Shape Loss Function for Point Cloud Completion

Shunran Zhang
Xiubo Zhang
Tsz Nam Chan
Shenghui Zhang
Leong Hou U

Learning-based point cloud completion tasks have shown potential in various critical tasks, such as object detection, assignment, and registration. However, accurately and efficiently quantifying the shape error between the predicted point clouds generated by networks and the ground truth remains challenging. While EMD-based loss functions excel in shape detail and perceived density distribution, their approach can only yield results with significant discrepancies from the actual EMD within a tolerable training time. To address these challenges, we first propose the initial price based on the auction algorithm, reducing the number of iterations required for the algorithm while ensuring the correctness of the assignment results. We then introduce an algorithm to compute the initial price through a successive shortest path and the Euclidean information between its nodes. Finally, we adopt a series of optimization strategies to speed up the algorithm and offer an EMD approximation scheme for point cloud problems that balances time loss and computational accuracy based on point cloud data characteristics. Our experimental results confirm that our algorithm achieves the smallest gap with the real EMD within an acceptable time range and yields the best results in end-to-end training.

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NeurIPS Conference 2022 Conference Paper

Redundancy-Free Message Passing for Graph Neural Networks

Rongqin Chen
Shenghui Zhang
Leong Hou U
Ye Li

Graph Neural Networks (GNNs) resemble the Weisfeiler-Lehman (1-WL) test, which iteratively update the representation of each node by aggregating information from WL-tree. However, despite the computational superiority of the iterative aggregation scheme, it introduces redundant message flows to encode nodes. We found that the redundancy in message passing prevented conventional GNNs from propagating the information of long-length paths and learning graph similarities. In order to address this issue, we proposed Redundancy-Free Graph Neural Network (RFGNN), in which the information of each path (of limited length) in the original graph is propagated along a single message flow. Our rigorous theoretical analysis demonstrates the following advantages of RFGNN: (1) RFGNN is strictly more powerful than 1-WL; (2) RFGNN efficiently propagate structural information in original graphs, avoiding the over-squashing issue; and (3) RFGNN could capture subgraphs at multiple levels of granularity, and are more likely to encode graphs with closer graph edit distances into more similar representations. The experimental evaluation of graph-level prediction benchmarks confirmed our theoretical assertions, and the performance of the RFGNN can achieve the best results in most datasets.

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