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Deep Learning for Graph Management and Analytics
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Deep Learning for Graph Management and Analytics

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 May 2025 | Viewed by 9562

Special Issue Editors

Prof. Dr. Xin Wang

E-Mail Website
Guest Editor
College of Intelligence and Computing, Tianjin University, Tianjin, China
Interests: knowledge graphs; graph databases; big data; distributed processing
Special Issues, Collections and Topics in MDPI journals
Dr. Guanfeng Liu

E-Mail Website
Guest Editor
Department of Computing, Macquarie University, Sydney, NSW 2109, Australia
Interests: graph data mining; social networks; trust computing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiang Zhao

E-Mail Website
Guest Editor
Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410073, China
Interests: graph; knowledge graph; graph theory; probability; computational complexity; data mining; natural language processing; set theory; approximation theory; business data processing; data handling; data integration; data structures; evolution

Special Issue Information

Dear Colleagues,

Deep learning is of utmost importance in the field of data management and analytics due to the vast amount of data generated and the complex computational tasks involved. There is a recent trend to establish foundation models on various kinds of data, enabling researchers to carry out complex reasoning and simulations. In the field of graph management and analytics, deep learning has been playing a crucial role. With deep learning, graph database query, graph generation, link prediction and other tasks can be done more efficiently and accurately. Moreover, deep learning models can also automatically discover patterns, relationships, and trends to give us deeper insights. This has huge implications for tasks such as social network analysis, social media sentiment analysis, and financial market forecasting.

For the last decade, the application of deep learning techniques has significantly improved the accuracy and efficiency of graph-based analysis, while also opening up new possibilities for data-driven decision-making and problem-solving. Despite the significant advantages of deep learning in graph management and analysis, there are still some challenges, including automatically or semi-automatically acquiring and annotating data, reducing the consumption of computing resources and memory, protecting data privacy, etc. The purpose of this special issue is to promote high-quality research on empowering graph management and analytics by deep learning and foundation models, to support existing and emerging applications, and to stimulate related research efforts.

Topics of interest include, but are not limited to, the following:

  • Big Graph Mining
  • Automatic Graph Acquisition
  • AI for Graph Databases
  • Graph Data for AI
  • Large-scale Graph Learning
  • Querying and Retrieval over Graphs
  • Foundation Models and LLMs

Prof. Dr. Xin Wang
Dr. Guanfeng Liu
Dr. Xiang Zhao
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 data
  • deep learning
  • graph management
  • graph analytics
  • graph algorithms

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Published Papers (7 papers)

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Research

20 pages, 8242 KiB  
Article
A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
by Yougui Ren, Zhibin Zhao, Junjian Jiang, Yuning Jiao, Yining Yang, Dawei Liu, Kefu Chen and Ge Yu
Appl. Sci. 2024, 14(18), 8535; https://doi.org/10.3390/app14188535 - 22 Sep 2024
Viewed by 797
Abstract
With the rapid development of remote sensing image data, the efficient retrieval of target images of interest has become an important issue in various applications including computer vision and remote sensing. This research addressed the low-accuracy problem in traditional content-based image retrieval algorithms, [...] Read more.
With the rapid development of remote sensing image data, the efficient retrieval of target images of interest has become an important issue in various applications including computer vision and remote sensing. This research addressed the low-accuracy problem in traditional content-based image retrieval algorithms, which largely rely on comparing entire image features without capturing sufficient semantic information. We proposed a scene graph similarity-based remote sensing image retrieval algorithm. Firstly, a one-shot object detection algorithm was designed for remote sensing images based on Siamese networks and tailored to the objects of an unknown class in the query image. Secondly, a scene graph construction algorithm was developed, based on the objects and their attributes and spatial relationships. Several construction strategies were designed based on different relationships, including full connections, random connections, nearest connections, star connections, or ring connections. Thirdly, by making full use of edge features for scene graph feature extraction, a graph feature extraction network was established based on edge features. Fourthly, a neural tensor network-based similarity calculation algorithm was designed for graph feature vectors to obtain image retrieval results. Fifthly, a dataset named remote sensing images with scene graphs (RSSG) was built for testing, which contained 929 remote sensing images with their corresponding scene graphs generated by the developed construction strategies. Finally, through performance comparison experiments with remote sensing image retrieval algorithms AMFMN, MiLaN, and AHCL, in precision rates, Precision@1 improved by 10%, 7.2%, and 5.2%, Precision@5 improved by 3%, 5%, and 1.7%; and Precision@10 improved by 1.7%, 3%, and 0.6%. In recall rates, Recall@1 improved by 2.5%, 4.3%, and 1.3%; Recall@5 improved by 3.7%, 6.2%, and 2.1%; and Recall@10 improved by 4.4%, 7.7% and 1.6%. Full article
(This article belongs to the Special Issue Deep Learning for Graph Management and Analytics)
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17 pages, 1633 KiB  
Article
ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
by Xinyu Du, Fangfang Li, Xiaohua Li and Ge Yu
Appl. Sci. 2024, 14(15), 6660; https://doi.org/10.3390/app14156660 - 30 Jul 2024
Viewed by 924
Abstract
Structural graph clustering is a data analysis technique that groups nodes within a graph based on their connectivity and structural similarity. The Structural graph clustering SCAN algorithm, a density-based clustering method, effectively identifies core points and their neighbors within areas of high density [...] Read more.
Structural graph clustering is a data analysis technique that groups nodes within a graph based on their connectivity and structural similarity. The Structural graph clustering SCAN algorithm, a density-based clustering method, effectively identifies core points and their neighbors within areas of high density to form well-defined clusters. However, the clustering quality of SCAN heavily depends on the input parameters, ϵ and μ, making the clustering results highly sensitive to parameter selection. Different parameter settings can lead to significant differences in clustering results, potentially compromising the accuracy of the clusters. To address this issue, a novel structural graph clustering algorithm based on the adaptive selection of density peaks is proposed in this paper. Unlike traditional methods, our algorithm does not rely on external parameters and eliminates the need for manual selection of density peaks or cluster centers by users. Density peaks are adaptively identified using the generalized extreme value distribution, with consideration of the structural similarities and interdependencies among nodes, and clusters are expanded by incorporating neighboring nodes, enhancing the robustness of the clustering process. Additionally, a distance-based structural similarity method is proposed to re-cluster noise nodes to the correct clusters. Extensive experiments on real and synthetic graph datasets validate the effectiveness of our algorithm. The experiment results show that the ADPSCAN has a superior performance compared with several state-of-the-art (SOTA) graph clustering methods. Full article
(This article belongs to the Special Issue Deep Learning for Graph Management and Analytics)
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22 pages, 2455 KiB  
Article
NeuChain+: A Sharding Permissioned Blockchain System with Ordering-Free Consensus
by Yuxiao Gao, Xiaohua Li, Zeshun Peng, Yanfeng Zhang and Ge Yu
Appl. Sci. 2024, 14(11), 4897; https://doi.org/10.3390/app14114897 - 5 Jun 2024
Viewed by 838
Abstract
Permissioned blockchains are widely used in scenarios such as digital assets, supply chains, government services, and Web 3.0, but their development is hindered by low throughput and scalability. Blockchain sharding addresses these issues by dividing the ledger into disjoint shards that can be [...] Read more.
Permissioned blockchains are widely used in scenarios such as digital assets, supply chains, government services, and Web 3.0, but their development is hindered by low throughput and scalability. Blockchain sharding addresses these issues by dividing the ledger into disjoint shards that can be processed concurrently. However, since cross-shard transactions require the collaboration of multiple shards, blockchain sharding needs a commit protocol to ensure the atomicity of executing these transactions, significantly impacting system performance. To this end, by exploiting the characteristics of deterministic ordering, we propose a cross-shard transaction processing protocol called cross-reserve, which eliminates this costly cross-shard coordination while providing the same consistency and atomicity guarantee. Based on the ordering-free execute–validate (EV) architecture, we implemented a blockchain prototype called NeuChain+, which further reduces the cross-shard transaction processing overhead using the pipelined read sets transmission. Experimental results show that NeuChain+ is scalable and outperforms state-of-the-art blockchain systems with 1.7–75.3× throughput under the SmallBank workload. Full article
(This article belongs to the Special Issue Deep Learning for Graph Management and Analytics)
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20 pages, 2432 KiB  
Article
RiQ-KGC: Relation Instantiation Enhanced Quaternionic Attention for Complex-Relation Knowledge Graph Completion
by Yunpeng Wang, Bo Ning, Shuo Jiang, Xin Zhou, Guanyu Li and Qian Ma
Appl. Sci. 2024, 14(8), 3221; https://doi.org/10.3390/app14083221 - 11 Apr 2024
Viewed by 880
Abstract
A knowledge graph is a structured semantic network designed to describe physical entities and relations in the world. A comprehensive and accurate knowledge graph is essential for tasks such as knowledge inference and recommendation systems, making link prediction a popular problem for knowledge [...] Read more.
A knowledge graph is a structured semantic network designed to describe physical entities and relations in the world. A comprehensive and accurate knowledge graph is essential for tasks such as knowledge inference and recommendation systems, making link prediction a popular problem for knowledge graph completion. However, existing approaches struggle to model complex relations among entities, which severely hampers their ability to complete knowledge graphs effectively. To address this challenge, we propose a novel hierarchical multi-head attention network embedding framework, called RiQ-KGC, which integrates different-grained contextual information of knowledge graph triples and models quaternion rotation relations between entities. Furthermore, we propose a relation instantiation method for alleviating the difficulty of expressing complex relations between entities. To enhance the expressiveness of relation representation, the relation is integrated by Transformer to obtain multi-hop neighbor information, so that one relation can be embedded into different embeddings according to different entities. Experimental results on four datasets demonstrate that RiQ-KGC exhibits strong competitiveness compared to state-of-the-art models in link prediction, while the ablation experiments reveal that the proposed relation instantiation method achieves great performance. Full article
(This article belongs to the Special Issue Deep Learning for Graph Management and Analytics)
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17 pages, 2399 KiB  
Article
FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP
by Fangling Leng, Fan Li, Yubin Bao, Tiancheng Zhang and Ge Yu
Appl. Sci. 2024, 14(8), 3154; https://doi.org/10.3390/app14083154 - 9 Apr 2024
Viewed by 2159
Abstract
As graph models become increasingly prevalent in the processing of scientific data, the exploration of effective methods for the mining of meaningful patterns from large-scale graphs has garnered significant research attention. This paper delves into the complexity of frequent subgraph mining and proposes [...] Read more.
As graph models become increasingly prevalent in the processing of scientific data, the exploration of effective methods for the mining of meaningful patterns from large-scale graphs has garnered significant research attention. This paper delves into the complexity of frequent subgraph mining and proposes a frequent subgraph mining (FSM) algorithm. This FSM algorithm is developed within a distributed graph iterative system, designed for the Big Cloud (BC) environment of the China Mobile Corp., and is based on the bulk synchronous parallel (BSP) model, named FSM-BC-BSP. Its aim is to address the challenge of mining frequent subgraphs within a single, large graph. This study advocates for the incorporation of a message sending and receiving mechanism to facilitate data sharing across various stages of the frequent subgraph mining algorithm. Additionally, it suggests employing a standard coded subgraph and sending it to the same node for global support calculation on the large graph. The adoption of the rightmost path expansion strategy in generating candidate subgraphs helps to mitigate the occurrence of redundant subgraphs. The use of standard coding ensures the unique identification of subgraphs, thus eliminating the need for isomorphism calculations. Support calculation is executed using the Minimum Image (MNI) measurement method, aligning with the downward closure attribute. The experimental results demonstrate the robust performance of the FSM-BC-BSP algorithm across diverse input datasets and parameter configurations. Notably, the algorithm exhibits exceptional efficacy, particularly in scenarios with low support requirements, showcasing its superior performance under such conditions. Full article
(This article belongs to the Special Issue Deep Learning for Graph Management and Analytics)
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21 pages, 4595 KiB  
Article
Memory-Enhanced Knowledge Reasoning with Reinforcement Learning
by Jinhui Guo, Xiaoli Zhang, Kun Liang and Guoqiang Zhang
Appl. Sci. 2024, 14(7), 3133; https://doi.org/10.3390/app14073133 - 8 Apr 2024
Cited by 1 | Viewed by 1659
Abstract
In recent years, the emergence of large-scale language models, such as ChatGPT, has presented significant challenges to research on knowledge graphs and knowledge-based reasoning. As a result, the direction of research on knowledge reasoning has shifted. Two critical issues in knowledge reasoning research [...] Read more.
In recent years, the emergence of large-scale language models, such as ChatGPT, has presented significant challenges to research on knowledge graphs and knowledge-based reasoning. As a result, the direction of research on knowledge reasoning has shifted. Two critical issues in knowledge reasoning research are the algorithm of the model itself and the selection of paths. Most studies utilize LSTM as the path encoder and memory module. However, when processing long sequence data, LSTM models may encounter the problem of long-term dependencies, where memory units of the model may decay gradually with an increase in time steps, leading to forgetting earlier input information. This can result in a decline in the performance of the LSTM model in long sequence data. Additionally, as the data volume and network depth increase, there is a risk of gradient disappearance. This study improved and optimized the LSTM model to effectively address the problems of gradient explosion and gradient disappearance. An attention layer was employed to alleviate the issue of long-term dependencies, and ConvR embedding was used to guide path selection and action pruning in the reinforcement learning inference model. The overall model achieved excellent reasoning results. Full article
(This article belongs to the Special Issue Deep Learning for Graph Management and Analytics)
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17 pages, 499 KiB  
Article
Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
by Yuxiao Duan, Jiuyang Tang, Hao Xu, Changsen Liu and Weixin Zeng
Appl. Sci. 2024, 14(5), 2044; https://doi.org/10.3390/app14052044 - 29 Feb 2024
Cited by 1 | Viewed by 982
Abstract
The inductive relation prediction of knowledge graphs, as an important research topic, aims at predicting the missing relation between unknown entities with many real-world applications. Existing approaches toward this problem mostly use enclosing subgraphs to extract the features of target nodes to make [...] Read more.
The inductive relation prediction of knowledge graphs, as an important research topic, aims at predicting the missing relation between unknown entities with many real-world applications. Existing approaches toward this problem mostly use enclosing subgraphs to extract the features of target nodes to make predictions; however, there is a tendency to ignore the neighboring relations outside the enclosing subgraph, thus leading to inaccurate predictions. In addition, they also neglect the rich commonsense information that can help filter out less convincing results. In order to address the above issues, this paper proposes a commonsense-guided inductive relation prediction method with a dual attention mechanism called CNIA. Specifically, in addition to the enclosing subgraph, we added the multi-hop neighboring relations of target nodes, thereby forming a neighbor-enriched subgraph where the initial embeddings are generated. Next, we obtained the subgraph representations with a dual attention (i.e., edge-aware and relation-aware) mechanism, as well as the neighboring relational path embeddings. Then, we concatenated the two embeddings before feeding them into the supervised learning model. A commonsense re-ranking mechanism was introduced to filter the results that conformed to commonsense. Extensive experiments on WN18RR, FB15k-237, and NELL995 showed that CNIA achieves better prediction results when compared to the state-of-the-art models. The results suggested that our proposed model can be considered as an effective and state-of-the-art solution for inductive relation prediction. Full article
(This article belongs to the Special Issue Deep Learning for Graph Management and Analytics)
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