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Hong-Ning Dai

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7 papers

1 author row

AAAI Conference 2026 Conference Paper

Gated Variational Graph Autoencoders as Experts with Competition and Consensus for Multi-view Clustering

Zhaoliang Chen
William K. Cheung
Hong-Ning Dai
Byron Choi
Jiming Liu

Multi-view clustering has been found useful to leverage diverse data sources for accurate and robust underlying data representations. It typically relies on effectively integrating the latent features from different views through allocating weights while simultaneously mining their specificity and consensus information. However, it remains open how to achieve a more fine-grained sample-level weight allocation for promoting view-specific information fusion and view-shared consensus. To address this problem, we propose a novel multi-expert learning framework named Gated Variational Graph AutoEncoder with Competition and Consensus (GVGAE-C2). In particular, it employs multiple view-specific Variational Graph AutoEncoders (VGAEs) as experts to capture the latent features from their own views. Furthermore, we design a fine-grained structure-aware gating network, which dynamically computes sample-level weights based on the proposed structure-aware quality evaluation on each expert, thus facilitating competition among experts. Meanwhile, each expert is trained not only to study its assigned view's specificity features, but also explicitly encouraged to learn consensus-aware features across views. Extensive multi-view clustering experiments on benchmark datasets reveal that GVGAE-C2 significantly outperforms state-of-the-art methods.

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AAAI Conference 2026 Conference Paper

Prior Refinement Is Better: Diffusion-Driven Graph Harmonization for Federated Graph Learning

Shuman Zhuang
Zhihao Wu
Wei Huang
Luojun Lin
Jia-Li Yin
Lele Fu
Hong-Ning Dai

Federated Graph Learning (FGL) has emerged as a compelling paradigm for collaboratively training a global model while preserving the privacy of multi-source graphs. Nonetheless, FGL faces a critical challenge of data heterogeneity, where semantic and structural discrepancies across clients significantly degrade its performance. Although existing methods attempt to calibrate client-specific graph distributions during federated training, they inevitably fall short in aligning the optimization behaviors across clients due to dynamic parameter updates, thereby inducing a bottleneck in generalization improvement. To tackle this challenge, we propose a solution from a new perspective of prior refinement, which seeks to proactively harmonize client graph distributions before the federated training. In particular, we propose a Federated Graph Harmonization (FedGH) framework that exploits the generative strengths of graph diffusion models to perform prior refinement of local graphs. In a nutshell, FedGH designs a conditional diffusion mechanism on each client that synthesizes pseudo-graphs encapsulating both feature and structural priors, thereby facilitating explicit correction of inter-client distributional bias. On the server side, we employ the graph contrastive learning between various client-specific pseudo-graphs to incorporate the global information, subsequently guiding local data reconstruction. Importantly, model-agnostic FedGH can be seamlessly deployed as a plug-and-play module to be easily integrated with existing FGL architectures. Extensive experiments demonstrate that FedGH consistently outperforms state-of-the-art FGL baselines.

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AAAI Conference 2026 Conference Paper

Talk2Code: A Multi-Turn Interaction Benchmark with Dual-Track Evaluation for Code Generation

Weibin Yang
Liangru Xie
Jieyun Cai
Yuxiang Yan
Hong-Ning Dai
Hao Wang

While large language models (LLMs) have demonstrated strong capabilities in code generation, current benchmarks primarily focus on single-turn scenarios, neglecting the complexity of multi-turn interactions and user diversity. To address this gap, we introduce Talk2Code, the first benchmark for user-stratified multi-turn dialogue code generation evaluation across algorithmic problem-solving and backend programming tasks.A distinctive feature of our benchmark is its user-stratified interaction modeling. For identical coding tasks, we construct dialogue trajectories tailored for novice, intermediate, and expert users, capturing their distinct expectations and communication patterns.To facilitate comprehensive evaluation, we propose a multi-dimensional evaluation framework assessing both code quality and interaction experience through a novel Dual-track Evaluation Method. In the Direct Generation Track, the benchmark provides golden dialogue context (excluding the final code) directly to the LLM for code generation. In contrast, the Interactive Dialogue Track simulates realistic multi-turn interactions, prompting the model to proactively clarify instructions and gather requirements before generating solutions. Code quality is evaluated in both tracks by Test Pass Rate and Success Rate, while interaction experience is assessed exclusively within the Interactive Dialogue Track through subjective and alignment indicators. Our benchmark and multi-dimensional indicator system collectively establish a new paradigm for evaluating adaptive, user-aware AI coding assistants.

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AAAI Conference 2025 Conference Paper

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning

Zhiqiang Li
Haiyong Bao
Menghong Guan
Hao Pan
Cheng Huang
Hong-Ning Dai

Despite federated learning (FL)'s potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning (CFL) has emerged to address this challenge by partitioning users into clusters according to their similarity. However, CFL faces difficulties in training when users are unwilling to share their cluster identities due to privacy concerns. To address these issues, we present an innovative Efficient and Robust Secure Aggregation scheme for CFL, dubbed EBS-CFL. The proposed EBS-CFL supports effectively training CFL while maintaining users' cluster identity confidentially. Moreover, it detects potential poisonous attacks without compromising individual client gradients by discarding negatively correlated gradients and aggregating positively correlated ones using a weighted approach. The server also authenticates correct gradient encoding by clients. EBS-CFL has high efficiency with client-side overhead O(ml + m^2) for communication and O(m^2l) for computation, where m is the number of cluster identities, and l is the gradient size. When m = 1, EBS-CFL's computational efficiency of client is at least O(log(n)) times better than comparison schemes, where n is the number of clients. In addition, we validate the scheme through extensive experiments. Finally, we theoretically prove the scheme's security.

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AAAI Conference 2025 Conference Paper

Refine then Classify: Robust Graph Neural Networks with Reliable Neighborhood Contrastive Refinement

Shuman Zhuang
Zhihao Wu
Zhaoliang Chen
Hong-Ning Dai
Ximeng Liu

Graph Neural Networks (GNNs) have exhibited remarkable capabilities for dealing with graph-structured data. However, recent studies have revealed their fragility to adversarial attacks, where imperceptible perturbations to the graph structure can easily mislead predictions. To enhance adversarial robustness, some methods attempt to learn robust representation through improving GNN architectures. Subsequently, another approach suggests that these GNNs might taint feature information and have poor classifier performance, leading to the introduction of Graph Contrastive Learning (GCL) methods to build a refining-classifying pipeline. However, existing methods focus on global-local contrastive strategies, which fails to address the robustness issues inherent in the contexts of adversarial robustness. To address these challenges, we propose a novel paradigm named GRANCE to enhance the robustness of learned representations by shifting the focus to local neighborhoods. Specifically, a dual neighborhood contrastive learning strategy is designed to extract local topological and semantic information. Paired with a neighbor estimator, the strategy can learn robust representations that are resilient to adversarial edges. Additionally, we also provide an improved GNN as classifier. Theoretical analyses provide a stricter lower bound of mutual information, ensuring the convergence of GRANCE. Extensive experiments validate the effectiveness of GRANCE compared to state-of-the-art baselines against various adversarial attacks.

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AAAI Conference 2023 Conference Paper

ODE-RSSM: Learning Stochastic Recurrent State Space Model from Irregularly Sampled Data

Zhaolin Yuan
Xiaojuan Ban
Zixuan Zhang
Xiaorui Li
Hong-Ning Dai

For the complicated input-output systems with nonlinearity and stochasticity, Deep State Space Models (SSMs) are effective for identifying systems in the latent state space, which are of great significance for representation, forecasting, and planning in online scenarios. However, most SSMs are designed for discrete-time sequences and inapplicable when the observations are irregular in time. To solve the problem, we propose a novel continuous-time SSM named Ordinary Differential Equation Recurrent State Space Model (ODE-RSSM). ODE-RSSM incorporates an ordinary differential equation (ODE) network (ODE-Net) to model the continuous-time evolution of latent states between adjacent time points. Inspired from the equivalent linear transformation on integration limits, we propose an efficient reparameterization method for solving batched ODEs with non-uniform time spans in parallel for efficiently training the ODE-RSSM with irregularly sampled sequences. We also conduct extensive experiments to evaluate the proposed ODE-RSSM and the baselines on three input-output datasets, one of which is a rollout of a private industrial dataset with strong long-term delay and stochasticity. The results demonstrate that the ODE-RSSM achieves better performance than other baselines in open loop prediction even if the time spans of predicted points are uneven and the distribution of length is changeable. Code is availiable at https://github.com/yuanzhaolin/ODE-RSSM.

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TIST Journal 2019 Journal Article

Lightweight Convolution Neural Networks for Mobile Edge Computing in Transportation Cyber Physical Systems

Junhao Zhou
Hong-Ning Dai
Hao Wang

Cloud computing extends Transportation Cyber-Physical Systems (T-CPS) with provision of enhanced computing and storage capability via offloading computing tasks to remote cloud servers. However, cloud computing cannot fulfill the requirements such as low latency and context awareness in T-CPS. The appearance of Mobile Edge Computing (MEC) can overcome the limitations of cloud computing via offloading the computing tasks at edge servers in approximation to users, consequently reducing the latency and improving the context awareness. Although MEC has the potential in improving T-CPS, it is incapable of processing computational-intensive tasks such as deep learning algorithms due to the intrinsic storage and computing-capability constraints. Therefore, we design and develop a lightweight deep learning model to support MEC applications in T-CPS. In particular, we put forth a stacked convolutional neural network (CNN) consisting of factorization convolutional layers alternating with compression layers (namely, lightweight CNN-FC). Extensive experimental results show that our proposed lightweight CNN-FC can greatly decrease the number of unnecessary parameters, thereby reducing the model size while maintaining the high accuracy in contrast to conventional CNN models. In addition, we also evaluate the performance of our proposed model via conducting experiments at a realistic MEC platform. Specifically, experimental results at this MEC platform show that our model can maintain the high accuracy while preserving the portable model size.

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