Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Machine Learning for Graph Pattern Mining and Its Applications
share announcement

Machine Learning for Graph Pattern Mining and Its 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: closed (30 November 2023) | Viewed by 3486

Special Issue Editors

Prof. Dr. Xiaoke Ma

E-Mail Website
Guest Editor
School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Interests: graph representation learning; graph mining; machine learning; bioinformatics
Dr. Jingjing Deng

E-Mail Website
Guest Editor
The Palatine Centre, Durham University, Durham DH1 3LE, UK
Interests: deep learning; graph learning; image processing

Special Issue Information

Dear Colleagues,

This Special Issue aims to collect high-quality review papers from the fields of graph mining research. We encourage researchers from various fields within the journal’s scope to contribute review papers highlighting the latest developments in their research field, or to invite relevant experts and colleagues to do so. Topics of interest for this Special Issue include, but are not limited to:

  • Graph representation learning
  • Graph clustering
  • Graph classification
  • Temporal networks
  • Multi-layer networks
  • Biological network analysis
  • Link prediction
  • Applications of graphs

Prof. Dr. Xiaoke Ma
Dr. Jingjing Deng
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.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

14 pages, 685 KiB  
Article
Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
by Li Li, Xiangquan Gui and Rui Lv
Appl. Sci. 2024, 14(1), 353; https://doi.org/10.3390/app14010353 - 30 Dec 2023
Viewed by 649
Abstract
Heterogeneous information networks (HINs) contain a rich network structure and semantic information, which makes them commonly used in recommendation systems. However, most of the existing HIN-based recommendation systems rely on meta-paths for information extraction, lack meta-path information supplements, and rarely learn complex structure [...] Read more.
Heterogeneous information networks (HINs) contain a rich network structure and semantic information, which makes them commonly used in recommendation systems. However, most of the existing HIN-based recommendation systems rely on meta-paths for information extraction, lack meta-path information supplements, and rarely learn complex structure information in heterogeneous graphs. To address these issues, we develop a novel recommendation algorithm that integrates the attention mechanism, meta-paths, and neighbor node information (AMNRec). In the heterogeneous information network, the missing information of the meta-path is supplemented by extracting the information of users and items’ neighbor nodes. The rich interactions between nodes are captured through convolution, and the embedded representation of nodes and meta-paths is obtained through the attention mechanism. TOP-N recommendation is completed by combining users, items, neighbor nodes, and meta-paths. Experiments on three public datasets show that AMNRec not only has the best recommendation performance but also has good interpretability of the recommendation results compared with the six recommendation benchmark algorithms. Full article
(This article belongs to the Special Issue Machine Learning for Graph Pattern Mining and Its Applications)
►▼ Show Figures

Figure 1

11 pages, 875 KiB  
Article
An Interactive Learning Network That Maintains Sentiment Consistency in End-to-End Aspect-Based Sentiment Analysis
by Musheng Chen, Qingrong Hua, Yaojun Mao and Junhua Wu
Appl. Sci. 2023, 13(16), 9327; https://doi.org/10.3390/app13169327 - 17 Aug 2023
Viewed by 654
Abstract
Most of the aspect-based sentiment analysis research completes the two subtasks (aspect terms extraction and aspect sentiment classification) separately, and it cannot see the full picture and actual effect of the complete aspect-based sentiment analysis. The purpose of end-to-end aspect-based sentiment analysis is [...] Read more.
Most of the aspect-based sentiment analysis research completes the two subtasks (aspect terms extraction and aspect sentiment classification) separately, and it cannot see the full picture and actual effect of the complete aspect-based sentiment analysis. The purpose of end-to-end aspect-based sentiment analysis is to complete the two subtasks of aspect terms extraction and aspect sentiment classification at the same time, and the current research in this area focuses on the connection between the two subtasks and uses the connection between them to construct the model. However, they rarely pay attention to the connection between different aspects and ignore the sentiment inconsistency within the aspects caused by the end-to-end model. Therefore, we propose an interactive learning network to maintain sentiment consistency, first using the multi-head attention mechanism to achieve the interaction between aspects and subtasks and then using the gate mechanism to design an auxiliary module to maintain sentiment consistency within aspect items. The experimental results on the datasets Laptop14, Restaurant14, and Twitter showed that, compared with the optimal benchmark method, the F1 values of the proposed method increased by 0.4%, 1.21%, and 5.22%, respectively. This indicates that the proposed method can effectively consider the relationships between aspect items and maintain emotional consistency within the aspect items. Full article
(This article belongs to the Special Issue Machine Learning for Graph Pattern Mining and Its Applications)
►▼ Show Figures

Figure 1

20 pages, 3157 KiB  
Article
A Network Traffic Abnormal Detection Method: Sketch-Based Profile Evolution
by Junkai Yi, Shuo Zhang, Lingling Tan and Yongbo Tian
Appl. Sci. 2023, 13(16), 9087; https://doi.org/10.3390/app13169087 - 09 Aug 2023
Cited by 1 | Viewed by 1506
Abstract
Network anomaly detection faces unique challenges from dynamic traffic, including large data volume, few attributes, and human factors that influence it, making it difficult to identify typical behavioral characteristics. To address this, we propose using Sketch-based Profile Evolution (SPE) to detect network traffic [...] Read more.
Network anomaly detection faces unique challenges from dynamic traffic, including large data volume, few attributes, and human factors that influence it, making it difficult to identify typical behavioral characteristics. To address this, we propose using Sketch-based Profile Evolution (SPE) to detect network traffic anomalies. Firstly, the Traffic Graph (TG) of the network terminal is generated using Sketch to identify abnormal data flow positions. Next, the Convolutional Neural Network and Long Short-Term Memory Network (CNN-LSTM) are used to develop traffic behavior profiles, which are then continuously updated using Evolution to detect behavior pattern changes in real-time data streams. SPE allows for direct processing of raw traffic datasets and continuous detection of constantly updated data streams. In experiments using real network traffic datasets, the SPE algorithm was found to be far more efficient and accurate than PCA and Basic Evolution for outlier detection. It is important to note that the value of φ can affect the results of anomaly detection. Full article
(This article belongs to the Special Issue Machine Learning for Graph Pattern Mining and Its Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop