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Graph Neural Networks: Methods and Applications in Graph-related Problems

Submission Deadline: 31 December 2024 View: 400 Submit to Special Issue

Guest Editors

Prof. Yanming Shen, Dalian University of Technology, China
Prof. Yong Zhang, Beijing University of Technology, China
Prof. Feng Cheng, University of Potsdam, Germany
Prof. Guorui Li, Northeastern University, China

Summary

Graph Neural Networks (GNNs) have emerged as a cutting-edge framework for directly analyzing graph-structured data using deep learning techniques. Their remarkable performance has inspired extensive exploration and research interest among scholars. GNNs excel in transforming graph data into standardized representations, enabling them to effectively address tasks such as node classification, edge information propagation, and graph clustering. What distinguishes GNNs from traditional deep learning methods is their distinctive capacity to capture intrinsic patterns and deeper semantic features within graphs. This unique capability markedly boosts their accuracy and robustness across a wide spectrum of graph-related challenges, establishing them as a highly promising approach in contemporary machine learning.

 

The proposed special issue aims to explore the diverse methods and applications of GNNs across multiple domains, including materials, transportation, meteorology, and beyond. By addressing challenges and harnessing the potential of GNNs, this special issue endeavors to uncover innovative approaches to enhance GNNs, providing a platform to integrate domain-specific knowledge and fully exploit their potential in real-world scenarios across diverse domains.

 

The potential topics include but are not limited to:

Heterogeneous Graphs and Their Applications

Dynamic Graphs and Their Applications

Hypergraphs and Their Applications

Spatio-Temporal Graphs and Their Applications

Multi-Task GNNs and Their Applications

Explainability on GNNs and Their Applications

Generative and Pre-trained Models for GNNs and Their Applications

Large-scale Graphs and Their Applications

Graph Foundation Models and Their Applications


Keywords

Graph Neural Networks;
Graph Representation Learning;
Graph-Structured Data;
Deep Learning Models for Graphs;
Applications of Graph Neural Networks

Published Papers


  • Open Access

    ARTICLE

    GATiT: An Intelligent Diagnosis Model Based on Graph Attention Network Incorporating Text Representation in Knowledge Reasoning

    Yu Song, Pengcheng Wu, Dongming Dai, Mingyu Gui, Kunli Zhang
    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4767-4790, 2024, DOI:10.32604/cmc.2024.053506
    (This article belongs to the Special Issue: Graph Neural Networks: Methods and Applications in Graph-related Problems)
    Abstract The growing prevalence of knowledge reasoning using knowledge graphs (KGs) has substantially improved the accuracy and efficiency of intelligent medical diagnosis. However, current models primarily integrate electronic medical records (EMRs) and KGs into the knowledge reasoning process, ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the text. To better integrate EMR text information, we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning (GATiT), which comprises text representation, subgraph construction, knowledge reasoning, and diagnostic classification. In the… More >

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