Feature Mining and Node Prediction for Complex Correlation Datasets Based on Graph Neural Networks

Enhancing link prediction in complex networks using GraphSAGE with graph diffusion convolution

A diffusion-enhanced inductive link prediction framework that combines Graph Diffusion Convolution (GDC), structural node descriptors, and neighborhood aggregation from GraphSAGE is proposed that achieves higher accuracy than the other models on the benchmark datasets.

A Hybrid Deep Learning Approach for Influential Nodes Identification in Complex Networks

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.

Conflicting Node Discrimination Graph Neural Network for Semi-supervised Node Classification

Graph Neural Networks (GNNs) have exhibited extraordinary performance in data mining. The success depends on the message passing mechanism, which is consistent with the homophily assumption that adjacent nodes share similar behaviors. However, the connected nodes in real-world graphs often show dissimilar behaviors, which is presented as the heterophily problem. A series of works proposed various models to solve the heterophily problem. They usually assume that the neighborhood distribution of nodes with the same class tends to be similar, while distinguishable from nodes of different classes. However, our experiments discovered that many nodes belonging to different classes share the same neighbor distribution in the mainstream datasets. The performance of GNN also degrades in these conflicting nodes. To detect the conflicting nodes and improve their prediction accuracy, we propose the Conflicting Node Discrimination GNN (CNDGNN). It firstly detects the possible conflicting nodes with the same neighbor distribution according to the selected high-quality pseudo-label. Then it estimates the class type of conflicting nodes with the same neighbor distribution through nodes' high-order neighbor distributions and original features, and the aggregation weights are learned to assign positive weights for nodes of the same class type and negative weights for nodes of different classes. The theoretical analysis proves that CNDGNN performs better than GCN in the conflicting nodes and explores the key design view when solving the conflicting node problem. The experiments demonstrate the effectiveness of our method in the mainstream datasets and verify that our method performs better in conflicting nodes.

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

The proposed AFGNN model dynamically regulates the fusion of view-specific embeddings, enhances reasoning capability, and suppresses uncertainty during representation learning, and demonstrates the effectiveness and superiority of AFGNN compared with state-of-the-art GNN-based link prediction models.

Inductive Correlation Clustering with Graph Neural Networks

Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and does not require a pre-specified number of clusters. Given $n$ objects and a pairwise similarity function, the goal is to cluster the objects so that similar objects are put in the same cluster and dissimilar objects are put in different clusters. Despite its versatility, existing CC algorithms suffer from significant scalability issues and are inherently transductive: i.e., the algorithm must be executed from scratch for any new problem instance. In this work, we bridge this gap by leveraging Graph Neural Networks (GNNs) to solve Inductive Correlation Clustering, a novel generalization of the CC problem designed to handle unseen graph instances. By learning to exploit common structural patterns and node features during training, our framework generalizes to new graphs drawn from the same distribution with minimal computational overhead with respect to standard algorithms. We demonstrate the effectiveness and scalability of our approach through extensive experiments. Our framework not only excels in the inductive setting, e.g., lowering the inference time up to $5$ order of magnitude, while maintaining an approximation ratio within $~10\%$ of the best baseline solution, but also achieves competitive results on standard (transductive) CC benchmarks. Finally, we showcase a practical application of our framework as a learnable pooling mechanism for graph classification. Our results indicate that our method serves as an efficient pooling layer, enhancing the ability of GNNs to capture hierarchical structural information in networks.