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Applied Sciences
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Graph-Based Methods in Artificial Intelligence and Machine Learning
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Graph-Based Methods in Artificial Intelligence and Machine Learning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 4575

Special Issue Editors

Prof. Dr. Barbara Strug

E-Mail Website
Guest Editor
Institute of Applied Computers Science, Jagiellonian University, 30-348 Kraków, Poland
Interests: knowledge representation; CAD; machine learning; BIM; graph-based computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Grażyna Ślusarczyk

E-Mail Website
Guest Editor
Institute of Applied Computers Science, Jagiellonian University, 30-348 Kraków, Poland
Interests: graph grammars; computer-aided graphic design; pattern recognition; diagrammatic reasoning; algorithm analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, graph structures have become an important research issue and attracted a lot of attention in many domains. There is also an increasing number of applications where data can naturally be represented by well-structured and flexible graph models, mainly due to their ability to encode both topological and semantic information about artefacts. Data can be represented by graphs in many different domains, such as scene graph generation and understanding, object tracking, point cloud classification, proteinomic and genomic data, text classification, relationships for both documents or words, natural language processing, traffic congestion, anomalies in networks, buildings in civil engineering, ontologies in different domains, scene and action in computer game design, and many more.

With these advances, graph structures became a new frontier in artificial intelligence and machine learning research. In many of the abovementioned domains, the adoption of graph neural network (GNN) models has been proven to be particularly effective, but other methods in AI and Ml have also been proven to be successful.

For this Special Issue, we look for papers dealing with both theoretical research and applications. The main subjects include but are not limited to:

  • Graph databases;
  • Graph-based versions of classical ML methods;
  • Graph neural networks (GNN);
  • Advanced graph models;
  • Learning on graphs;
  • Graph data management;
  • Graph mining;
  • Graph kernels;
  • Knowledge graphs;
  • Applications of graph models in engineering, computer vision, graphics, architecture, arts, ecommerce, natural language processing (NLP), computer games, music, etc.

Prof. Dr. Barbara Strug
Prof. Dr. Grażyna Ślusarczyk
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • graph representation
  • GNN
  • graph data mining
  • graph data management
  • graph databases
  • graph machine learning

Published Papers (5 papers)

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Research

18 pages, 3128 KiB  
Article
Advancing Brain Tumor Segmentation with Spectral–Spatial Graph Neural Networks
by Sina Mohammadi and Mohamed Allali
Appl. Sci. 2024, 14(8), 3424; https://doi.org/10.3390/app14083424 - 18 Apr 2024
Viewed by 303
Abstract
In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial [...] Read more.
In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial insight. In the supervoxel creation phase, we explored methods like VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb–Huttenlocher, evaluating their performance based on homogeneity, moment of inertia, and uniformity in shape and size. After creating supervoxels, we represented 3D MRI images as a graph structure. In this study, we combined Spatial and Spectral GNNs to capture both local and global information. Our Spectral GNN implementation employs the Laplacian matrix to efficiently map tumor tissue connectivity by capturing the graph’s global structure. Consequently, this enhances the model’s precision in classifying brain tumors into distinct types: necrosis, edema, and enhancing tumor. This model underwent extensive hyper-parameter tuning to ascertain the most effective configuration for optimal segmentation performance. Our Spectral–Spatial GNN model surpasses traditional segmentation methods in accuracy for both whole tumor and sub-regions, validated by metrics such as the dice coefficient and accuracy. For the necrotic core, the Spectral–Spatial GNN model showed a 10.6% improvement over the Spatial GNN and 8% over the Spectral GNN. Enhancing tumor gains were 9.5% and 6.4%, respectively. For edema, improvements were 12.8% over the Spatial GNN and 7.3% over the Spectral GNN, highlighting its segmentation accuracy for each tumor sub-region. This superiority underscores the model’s potential in improving brain tumor segmentation accuracy, precision, and computational efficiency. Full article
(This article belongs to the Special Issue Graph-Based Methods in Artificial Intelligence and Machine Learning)
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19 pages, 2713 KiB  
Article
Product Space Clustering with Graph Learning for Diversifying Industrial Production
by Kévin Cortial, Adélaïde Albouy-Kissi and Frédéric Chausse
Appl. Sci. 2024, 14(7), 2833; https://doi.org/10.3390/app14072833 - 27 Mar 2024
Viewed by 384
Abstract
During economic crises, diversifying industrial production emerges as a critical strategy to address societal challenges. The Product Space, a graph representing industrial knowledge proximity, acts as a valuable tool for recommending diversified product offerings. These recommendations rely on the edges of the graph [...] Read more.
During economic crises, diversifying industrial production emerges as a critical strategy to address societal challenges. The Product Space, a graph representing industrial knowledge proximity, acts as a valuable tool for recommending diversified product offerings. These recommendations rely on the edges of the graph to identify suitable products. They can be improved by grouping similar products together, which results in more precise suggestions. Unlike the topology, the textual data in nodes of the Product Space graph are typically unutilized in graph clustering methods. In this context, we propose a novel approach for economic graph learning that incorporates learning node data alongside network topology. By applying this method to the Product Space dataset, we demonstrate how recommendations have been improved by presenting real-life applications. Our research employing a graph neural network demonstrates superior performance compared to methods like Louvain and I-Louvain. Our contribution introduces a node data-based deep graph clustering graph neural network that significantly advances the macroeconomic literature and addresses the imperative of diversifying industrial production. We discuss both the advantages and limitations of deep graph learning models in economics, laying the groundwork for future research. Full article
(This article belongs to the Special Issue Graph-Based Methods in Artificial Intelligence and Machine Learning)
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15 pages, 2730 KiB  
Article
A Multi-Agent System in Education Facility Design
by Barbara Strug and Grażyna Ślusarczyk
Appl. Sci. 2023, 13(19), 10878; https://doi.org/10.3390/app131910878 - 30 Sep 2023
Viewed by 581
Abstract
This paper deals with a multi-agent system which supports the designer in solving complex design tasks. The behaviour of design agents is modelled by sets of grammar rules. Each agent uses a graph grammar or a shape grammar and a database of facts [...] Read more.
This paper deals with a multi-agent system which supports the designer in solving complex design tasks. The behaviour of design agents is modelled by sets of grammar rules. Each agent uses a graph grammar or a shape grammar and a database of facts concerning the subtask it is responsible for. The course of the design process is determined by the interaction between specialised agents. Space layouts of designs are represented by attributed graphs encoding both topological structures and semantic properties of solutions. The agents work in parallel on the common graph, independently generating layouts of different design components while specified node labels evoke agents using shape grammars. The agents’ cooperation allows them to combine a form-oriented approach with a functional-structural one in the design process, where the agents generate the general 3D form of the object based on design requirements together with the space layout based on the functional aspects of the solution. Based on the given design criteria, the agents search for admissible solutions within the design space that constitutes their operating environment. The proposed approach is illustrated by the example of designing kindergarten facilities. Full article
(This article belongs to the Special Issue Graph-Based Methods in Artificial Intelligence and Machine Learning)
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19 pages, 1107 KiB  
Article
Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
by Sirui Shen, Daobin Zhang, Shuchao Li, Pengcheng Dong, Qing Liu, Xiaoyu Li and Zequn Zhang
Appl. Sci. 2023, 13(6), 3989; https://doi.org/10.3390/app13063989 - 21 Mar 2023
Viewed by 1237
Abstract
Heterogeneous graph neural networks (HGNNs) deliver the powerful capability to model many complex systems in real-world scenarios by embedding rich structural and semantic information of a heterogeneous graph into low-dimensional representations. However, existing HGNNs encounter great difficulty in balancing the ability to avoid [...] Read more.
Heterogeneous graph neural networks (HGNNs) deliver the powerful capability to model many complex systems in real-world scenarios by embedding rich structural and semantic information of a heterogeneous graph into low-dimensional representations. However, existing HGNNs encounter great difficulty in balancing the ability to avoid artificial metapaths with resisting structural and informational noise in a heterogeneous graph. In this paper, we propose a novel framework called Heterogeneous Graph Purification Network (HGPN) which aims to solve such dilemma by adaptively purifying the noisy heterogeneity. Specifically, instead of relying on artificial metapaths, HGPN models heterogeneity by subgraph decomposition and adopts inter-subgraph and intra-subgraph aggregation methods. HGPN can learn to purify noisy edges based on semantic information with a parallel heterogeneous structure purification mechanism. Besides, we design a neighborhood-related dynamic residual update method, a type-specific normalization module and cluster-aware loss to help all types of node achieve high-quality representations and maintain feature distribution while preventing feature over-mixing problems. Extensive experiments are conducted on four common heterogeneous graph datasets, and results show that our approach outperforms all existing methods and achieves state-of-the-art performances consistently among all the datasets. Full article
(This article belongs to the Special Issue Graph-Based Methods in Artificial Intelligence and Machine Learning)
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12 pages, 2067 KiB  
Article
Research on the Prediction of Operator Users’ Number Portability Based on Community Detection
by Ruixia Chen and Binmei Liang
Appl. Sci. 2023, 13(6), 3497; https://doi.org/10.3390/app13063497 - 09 Mar 2023
Viewed by 847
Abstract
In 2019, China introduced a policy on Number Portability Management, which has resulted in a rapid increase in the number of lost users among telecom companies. Telecom companies must urgently distinguish those with a tendency toward number portability. However, existing prediction research lacks [...] Read more.
In 2019, China introduced a policy on Number Portability Management, which has resulted in a rapid increase in the number of lost users among telecom companies. Telecom companies must urgently distinguish those with a tendency toward number portability. However, existing prediction research lacks the input of temporal variations in user data and the graph-based analysis of user relationship characteristics, resulting in a poor prediction effect. In this paper, a neural-network-based approach has been applied to address the limitation, whereby user data do not feature temporal variation. Furthermore, innovative approaches have been proposed to construct multilayer community networks through users’ geographic attributes and to analyze community networks with a network embedding method based on the matrix factorization framework. This fills a gap in existing research areas, whereby the geographic attributes of users have not received much attention. Considering the extensive inputs and multiple features of the predicted attributes, in this paper, the strengths and weaknesses of three feature selection methods are compared, as well as the prediction accuracy of each of the five prediction models. Finally, the embedded feature selection method, deep neural network model, and the Light GBM model are shown to provide better results. After introducing the user community network, it was found that the prediction evaluation indicators of both the deep neural network model and the Light GBM model are improved. Full article
(This article belongs to the Special Issue Graph-Based Methods in Artificial Intelligence and Machine Learning)
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