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Phu Pham

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Jul 2026

Multi‐Viewed Graph Representation Learning Through Graph Neural Network and Rich‐Spatial Local Feature Embedding

For many years, graph representation learning plays a pivotal role in bioinformatics and cheminformatics; as a result, supporting a wide range of tasks such as drug discovery, toxicity prediction, and compound–protein interaction analysis. However, existing approaches often focus solely on either sequential molecular fingerprints or graph‐based structural features, which limit their ability to capture both local chemical substructures and global molecular topology. To address this issue, we propose MM2Vec, a novel multi‐viewed molecular representation learning framework that integrates local rich‐feature embedding with graph neural network (GNN)‐based structural learning. Specifically, each molecular graph is first processed through an MLP‐based embedding layer that encodes sub‐structural fingerprint information extracted from radius‐based subgraphs, capturing fine‐grained chemical and physiochemical features. Simultaneously, a multi‐layered GNN encoder learns topological relationships from the molecular graph structure; therefore, focusing more on geometric and relational information among atoms. The outputs from both embedding branches are then fused using a learnable linear mechanism to produce unified, high‐quality molecular embeddings in a shared latent space. These fused representations are used to drive task‐specific prediction layers for addressing various learning objectives. We validate the proposed MM2Vec model on multiple graph learning tasks, including drug‐induced liver injury (DILI) classification and lethal dose (LD) molecular regression problems. Experimental results show that MM2Vec consistently outperforms classical machine learning (ML)‐based models and recent state‐of‐the‐art deep learning (DL)/GNN‐based methods in terms of accuracy, robustness, and generalization. Our findings in this highlight the importance of combining both sub‐structural and graph‐structural perspectives and demonstrate the versatility and effectiveness of our MM2Vec model for a wide range of molecular analysis tasks.

Phu Pham · 0 citations
Jul 2026

Adaptive Inference Neuro-Fuzzy Driven Embedding Fusion for Improving Link Prediction Through Graph Neural Network

Link prediction has long been regarded as a fundamental problem in networked data analysis and mining, owing to its importance in a wide range of real-world applications, including social network analysis, recommendation systems, and biological network modeling. Over the years, numerous approaches have been proposed to address this problem, ranging from similarity-based heuristics and probabilistic models to dimensionality reduction and deep learning (DL)-based representation learning techniques. These methods aim to capture latent relational patterns among nodes in order to infer missing or potential links. Recently, graph neural networks (GNNs) have emerged as a powerful DL paradigm for graph-structured data representation learning and have demonstrated strong performance in complex link prediction tasks. In particular, multi-view GNN-based approaches have gained increasing attention by integrating different GNN architectures to capture complementary structural perspectives, such as local neighborhood proximity and global graph topology. This strategy enables the learning of richer node embeddings and improves task-driven prediction performance. However, the integration of multiple GNN layers and architectures often introduces feature uncertainty and noise amplification, which can degrade model stability and predictive accuracy. To address these challenges, this paper proposes AFGNN, a novel framework that integrates adaptive neuro-fuzzy inference with multi-view graph neural learning. By leveraging a robust fuzzy neural architecture with an adaptive inference engine, our proposed AFGNN model dynamically regulates the fusion of view-specific embeddings, enhances reasoning capability, and suppresses uncertainty during representation learning. Extensive experiments on benchmark graph datasets demonstrate the effectiveness and superiority of AFGNN compared with state-of-the-art GNN-based link prediction models.

Phu Pham · 0 citations