Shengda Zhuo

Shengda Zhuo, Di Wu, Yi He et al.

Online learning, where feature spaces can change over time, offers a flexible learning paradigm that has attracted considerable attention. However, it still faces three significant challenges. First, the heterogeneity of real-world data streams with mixed feature types presents challenges for traditional parametric modeling. Second, data stream distributions can shift over time, causing an abrupt and substantial decline in model performance. Additionally, the time and cost constraints make it infeasible to label every data instance in a supervised setting. To overcome these challenges, we propose a new algorithm Online Learning from Mix-typed, Drifted, and Incomplete Streaming Features (OL-MDISF), which aims to relax restrictions on both feature types, data distribution, and supervision information. Our approach involves utilizing copula models to create a comprehensive latent space, employing an adaptive sliding window for detecting drift points to ensure model stability, and establishing label proximity information based on geometric structural relationships. To demonstrate the model's efficiency and effectiveness, we provide theoretical analysis and comprehensive experimental results. This extension serves as a standalone technical reference to the original OL-MDISF method. It provides (i) a contextual analysis of OL-MDISF within the broader landscape of online learning, covering recent advances in mixed-type feature modeling, concept drift adaptation, and weak supervision, and (ii) a comprehensive set of experiments across 14 real-world datasets under two types of drift scenarios. These include full CER trends, ablation studies, sensitivity analyses, and temporal ensemble dynamics. We hope this document can serve as a reproducible benchmark and technical resource for researchers working on nonstationary, heterogeneous, and weakly supervised data streams.

Chu-Yuan Wei, Shun-Yao Liu, Sheng-Da Zhuo et al.

Graph Neural Networks (GNNs) have achieved remarkable success in various graph-based tasks (e.g., node classification or link prediction). Despite their triumphs, GNNs still face challenges such as long training and inference times, difficulty in capturing complex relationships, and insufficient feature extraction. To tackle these issues, graph pre-training and graph prompt methods have garnered increasing attention for their ability to leverage large-scale datasets for initial learning and task-specific adaptation, offering potential improvements in GNN performance. However, previous research has overlooked the potential of graph prompts in optimizing models, as well as the impact of both positive and negative graph prompts on model stability and efficiency. To bridge this gap, we propose a novel framework combining graph prompts with weight pruning, called GPAWP, which aims to enhance the performance and efficiency of graph prompts by using fewer of them. We evaluate the importance of graph prompts using an importance assessment function to determine positive and negative weights at different granularities. Through hierarchically structured pruning, we eliminate negative prompt labels, resulting in more parameter-efficient and competitively performing prompts. Extensive experiments on three benchmark datasets demonstrate the superiority of GPAWP, leading to a significant reduction in parameters in node classification tasks.

Xiaotong Luo, Shengda Zhuo, Min Chen et al.

Financial markets exhibit highly dynamic and complex behaviors shaped by both historical price trajectories and exogenous narratives, such as news, policy interpretations, and social media sentiment. The heterogeneity in these data and the diverse insight of investors introduce biases that complicate the modeling of market dynamics. Unlike prior work, this paper explores the potential of bull and bear regimes in investor-driven market dynamics. Through empirical analysis on real-world financial datasets, we uncover a dynamic relationship between bias variation and behavioral adaptation, which enhances trend prediction under evolving market conditions. To model this mechanism, we propose the Bias to Behavior from Bull-Bear Dynamics model (B4), a unified framework that jointly embeds temporal price sequences and external contextual signals into a shared latent space where opposing bull and bear forces naturally emerge, forming the foundation for bias representation. Within this space, an inertial pairing module pairs temporally adjacent samples to preserve momentum, while the dual competition mechanism contrasts bullish and bearish embeddings to capture behavioral divergence. Together, these components allow B4 to model bias-driven asymmetry, behavioral inertia, and market heterogeneity. Experimental results on real-world financial datasets demonstrate that our model not only achieves superior performance in predicting market trends but also provides interpretable insights into the interplay of biases, investor behaviors, and market dynamics.

Shengda Zhuo, Jiwang Fang, Hongguang Lin et al.

Graph Neural Networks (GNNs) have significant advantages in handling non-Euclidean data and have been widely applied across various areas, thus receiving increasing attention in recent years. The framework of GNN models mainly includes the information propagation phase and the aggregation phase, treating nodes and edges as information entities and propagation channels, respectively. However, most existing GNN models face the challenge of disconnection between node and edge feature information, as these models typically treat the learning of edge and node features as independent tasks. To address this limitation, we aim to develop an edge-empowered graph feature preference learning framework that can capture edge embeddings to assist node embeddings. By leveraging the learned multidimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features, thereby obtaining the non-local structural characteristics and fine-grained high-order node features. Specifically, the inclusion of multidimensional edge information enhances the functionality and flexibility of the GNN model, enabling it to handle complex and diverse graph data more effectively. Additionally, integrating relational representation learning into the message passing framework allows graph nodes to receive more useful information, thereby facilitating node representation learning. Finally, experiments on four real-world heterogeneous graphs demonstrate the effectiveness of theproposed model.