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Feiyu Yin

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JBHI Journal 2025 Journal Article

Adaptive Multi-Scale Dynamic Graph Representation Learning With Overlapping Community-Awareness for ASD Classification

Wenwen Zeng
Feiyu Yin
Pengfei Song
Yonghuang Wu
Chengqian Zhao
Guoqing Wu
Jinhua Yu

In recent years, dynamic functional connectivity (dFC) has been widely employed for brain disease diagnosis. By leveraging the inherent topological characteristics of the brain, graph neural networks (GNNs) have emerged as prominent deep learning methods for utilizing dFC in this context. However, existing research has some limitations. Temporally, the conventional fixed-length sliding window approach often fails to capture the multi-scale temporal characteristics inherent in brain activity. Spatially, GNN-derived graph representations usually overlook the multi-network participation of brain regions. To address these limitations, we propose Ada-MST, an adaptive multi-scale spatio-temporal model utilizing multi-scale dFC for brain disease diagnosis. Our framework constructs personalized multi-scale dFC graphs that adapt to subject-specific temporal characteristics. Moreover, we introduce a novel overlapping community-aware readout module that incorporates the participation of brain regions in multiple functional networks, leading to more accurate graph-level representations. Experiments on ABIDE-I and ABIDE-II datasets demonstrate that our method outperforms state-of-the-art approaches. Visualization analysis further confirms the generalizability of the subject-adaptive graphs and their focus on disease-related brain activity. Furthermore, the fuzzy memberships revealed by our readout module indicate distinct patterns across diseases, suggesting the promise of considering functional community membership changes for exploring disease biomarkers.

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AAAI Conference 2023 Conference Paper

Next POI Recommendation with Dynamic Graph and Explicit Dependency

Feiyu Yin
Yong Liu
Zhiqi Shen
Lisi Chen
Shuo Shang
Peng Han

Next Point-Of-Interest (POI) recommendation plays an important role in various location-based services. Its main objective is to predict the user's next interested POI based on her previous check-in information. Most existing methods directly use users' historical check-in trajectories to construct various graphs to assist sequential models to complete this task. However, as users' check-in data is extremely sparse, it is difficult to capture the potential relations between POIs by directly using these check-in data. To this end, we propose the Sequence-based Neighbour search and Prediction Model (SNPM) for next POI recommendation. In SNPM, the RotatE knowledge graph embedding and Eigenmap methods are used to extract POI relationships implied in check-in data, and build the POI similarity graph. Then, we enhance the model's generalized representations of POIs' general features by aggregating similar POIs. As the context is typically rich and valuable when making Next POI predictions, the sequence model selects which POIs to aggregate not only depends on the current state, but also needs to consider the previous POI sequence. Therefore, we construct a Sequence-based, Dynamic Neighbor Graph (SDNG) to find the similarity neighbourhood and develop a Multi-Step Dependency Prediction model (MSDP) inspired by RotatE, which explicitly leverage information from previous states. We evaluate the proposed model on two real-world datasets, and the experimental results show that the proposed method significantly outperforms existing state-of-the-art POI recommendation methods.

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