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Applied Sciences
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Graph Mining: Theories, Algorithms and Applications
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Graph Mining: Theories, Algorithms and Applications

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 September 2025 | Viewed by 1710

Special Issue Editor

Prof. Dr. Tao Zhou

E-Mail Website
Guest Editor
Complex Lab, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: complexity science; network science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to delve into the intricacies of graph data and facilitate the synergy of theoretical advancements with practical implementations. This dedicated space intends to foster interdisciplinary dialogue among researchers and practitioners in the fields of computer science, data science, network theory, physical science and applied mathematics, collectively pushing the frontiers of knowledge in graph mining.

The scope of this Special Issue primarily encompasses the challenges and developments associated with graph mining. This includes Link Prediction, which predicts the missing and future links in networks; Influential Node Identification, which uncovers the nodes that are pivotal in the spread of epidemics and the dissemination of information; Communication Detection, which deciphers patterns/clustering of interaction; and Frequent Subgraph Mining, which reveals recurring structures. We are also interested in the prediction of network evolution and processes, and the outcomes of networked dynamics. This Special Issue warmly welcomes contributions that address graph mining from diverse perspectives, encompassing investigations into fundamental problems that range from nuanced mechanistic models to large-scale machine learning algorithms, and extending to the profound applications of graph mining in real-world systems such as biology, sociology, and economics.

Prof. Dr. Tao Zhou
Guest Editor

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

  • complex networks
  • link prediction
  • influential node identification
  • communication detection
  • graph embedding
  • frequent subgraph mining
  • graph neural networks
  • graph generative models

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Published Papers (3 papers)

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Research

14 pages, 707 KiB  
Article
SR-GNN: A Signed Network Embedding Method Guided by Status Theory and a Reciprocity Relationship
by Xiaoping Su, Yinghua Zha and Fuquan Zhang
Appl. Sci. 2025, 15(8), 4520; https://doi.org/10.3390/app15084520 - 19 Apr 2025
Viewed by 183
Abstract
Many complex social systems can be modeled as directed signed networks whose edges are marked with a positive/negative sign or direction. Network embedding representation is aimed at mapping rich structural and semantic information into low-dimensional vectors, and extensive research has demonstrated that Graph [...] Read more.
Many complex social systems can be modeled as directed signed networks whose edges are marked with a positive/negative sign or direction. Network embedding representation is aimed at mapping rich structural and semantic information into low-dimensional vectors, and extensive research has demonstrated that Graph Neural Networks (GNNs) are an effective way. However, existing GNNs are primarily designed for undirected signed networks and usually used to capture the semantics of the complex structure by social structural balance theory, thus omitting the directional information of the links. In this research, we introduce a reciprocity relationship and status theory to enhance the modeling of the directed positive/negative relationship between two nodes, which has been widely applied in complex network research, and design SR-GNN, a GNN model for signed directed networks, to enable a more accurate vector representation of the nodes and convolution operations on edges with different directions and signs. Experiments demonstrate a reciprocity relationship, and status theory can allow the model to extract the most essential comprehensive information in signed directed graphs. Furthermore, SR-GNN can obtain effective status scores of nodes for link sign predictions and node ranking tasks, both of which yield state-of-the-art performance in most cases. Full article
(This article belongs to the Special Issue Graph Mining: Theories, Algorithms and Applications)
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23 pages, 9504 KiB  
Article
Automated Residential Bubble Diagram Generation Based on Dual-Branch Graph Neural Network and Variational Encoding
by Gan Luo, Xuhong Zhou, Yunzhu Liao, Yao Ding, Jiepeng Liu, Yi Xia and Hongtuo Qi
Appl. Sci. 2025, 15(8), 4490; https://doi.org/10.3390/app15084490 - 18 Apr 2025
Viewed by 264
Abstract
Bubble diagrams containing key features and information are used for generative design of floor plans. The lack of reliable methods for automatically generating bubble diagrams significantly affects the smoothness of layout generation systems. To improve the time-consuming and unstable acquisition process, a novel [...] Read more.
Bubble diagrams containing key features and information are used for generative design of floor plans. The lack of reliable methods for automatically generating bubble diagrams significantly affects the smoothness of layout generation systems. To improve the time-consuming and unstable acquisition process, a novel method based on graph neural networks (GNNs) is proposed to generate various residential bubble diagrams. First, a dual-branch graph neural network (DBGNN) is introduced to learn the feature patterns of heterogeneous links, including connectivity and adjacency relations. Then, decentralized node sampling (DNS) and centralized node sampling (CNS) are proposed to enhance the local feature learning of DBGNN. Subsequently, a variational graph autoencoder (VGAE) is used to learn the implicit distribution of topological patterns, enabling the model to generate diverse outputs. Experimental results show that the proposed model performs excellently in two link prediction tasks, achieving 92.39% ACC-Door and 78.84% ACC-Wall, while also generating 50 distinct bubble diagrams, validating the effectiveness of the proposed method and demonstrating its outstanding application value. Full article
(This article belongs to the Special Issue Graph Mining: Theories, Algorithms and Applications)
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24 pages, 834 KiB  
Article
Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
by Ocheme Anthony Ekle, William Eberle and Jared Christopher
Appl. Sci. 2025, 15(6), 3360; https://doi.org/10.3390/app15063360 - 19 Mar 2025
Viewed by 562
Abstract
Real-time anomaly detection in large, dynamic graph networks is crucial for real-world applications such as network intrusion prevention, fraud transaction identification, fake news detection in social networks, and uncovering abnormal communication patterns. However, existing graph-based methods often focus on static graph structures, which [...] Read more.
Real-time anomaly detection in large, dynamic graph networks is crucial for real-world applications such as network intrusion prevention, fraud transaction identification, fake news detection in social networks, and uncovering abnormal communication patterns. However, existing graph-based methods often focus on static graph structures, which struggle to adapt to the evolving nature of these graphs. In this paper, we propose Adaptive-DecayRank, a real-time and adaptive anomaly detection model for dynamic graph streams. Our method extends the dynamic PageRank algorithm by incorporating an adaptive Bayesian updating mechanism, allowing nodes to dynamically adjust their decay factors based on observed graph changes. This enables real-time detection of sudden structural shifts, improving anomaly identification in streaming graphs. We evaluate Adaptive-DecayRank on multiple real-world security datasets, including DARPA and CTU-13, as well as synthetic dense graphs generated using RTM. Our experiments demonstrate that Adaptive-DecayRank outperforms state-of-the-art methods, such as AnomRank, Sedanspot, and DynAnom, achieving up to 13.94% higher precision, 8.43% higher AUC, and more robust detection in highly dynamic environments. Full article
(This article belongs to the Special Issue Graph Mining: Theories, Algorithms and Applications)
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