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Special Issue "Information Network Mining and Applications"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 10 March 2023 | Viewed by 1239

Special Issue Editors

Dr. Yongpan Sheng
E-Mail Website
Guest Editor
College of Computer and Information Science, Southwest University, Chongqing 400715, China
Interests: textual data mining; knowledge graphs; graph representation learning; code understanding and representation
Dr. Hao Wang
E-Mail Website
Guest Editor
School of Computer Science and Technology, University of Science and Technology of China (USTC), Hefei 230027, China
Interests: data mining; graph neural network; graph representation learning; recommendation system; network embedding
Dr. Yixiang Fang
E-Mail Website
Guest Editor
School of Data Science, The Chinese University of Hong Kong, Shenzhen 518172, China
Interests: data management; data mining; cohesive subgraph search; graph embedding; graph neural network; keyword search; trajectory computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Information networks, also known as heterogeneous graphs, linguistics graphs, social networks, knowledge graphs, and so forth, which are able to consist of different types of entities and relations, have been attracting substantial attention in academia and industry in recent years. Advanced research on information network theory is essential to address the issues that relate to the structure modeling, representation, and applications of more complex, high-order information networks. Additionally, deep learning models on information networks have achieved remarkable performance in various tasks (e.g., graph representation learning, graph generation, and graph classification) when applied to domains such as knowledge graphs, social networks, bibliographic networks, traffic networks, and molecules. Despite these successes, as a promising network analysis paradigm, information network mining also is facing new challenges, such as how to manage typical networks such as multi-modal, multi-relational, and dynamic graphs, how to efficiently learn network/graph representation of large-scale information networks for preserving rich structural and semantic information, how to learn with limited labels on information networks, and how to mine knowledge in the information network, which is also of significance in solving sophisticated problems with more promising performance. 

This Special Issue is a forum for researchers from a variety of fields working on mining and learning from information networks to share and discuss their latest findings. It welcomes original algorithmic, methodological, theoretical, statistical, or systems-based contributions to information network research and, in particular, applications broadly related to knowledge graphs, social networks, stock prediction, online shopping, recommendation systems, self-driving car, bioinformatics and medical informatics. Research papers and comprehensive reviews may focus on (but are not restricted to) the following research areas:

  • Network/graph representation learning for homogeneous or heterogeneous information networks;
  • Network/graph modelling like multi-modal, multi-relational, and dynamic graphs;
  • Graph transformer and graph convolutional neural network;
  • Data mining based on knowledge graphs, linguistics graphs, bibliographic graphs, textual graphs, social networks, traffic networks, and molecules;
  • Parallel computing for information network analysis;
  • Visual searching and browsing of information networks;
  • Applications of information network mining in e-commerce, text mining, stock prediction, recommendation systems, self-driving car, bioinformatics and medical informatics, and so on;
  • Information networks for explainable AI.

Dr. Yongpan Sheng
Dr. Hao Wang
Dr. Yixiang Fang
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. Entropy is an international peer-reviewed open access monthly 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 1800 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.


  • information networks
  • network/graph representation learning
  • data mining
  • knowledge graphs
  • information network applications
  • explainable AI

Published Papers (1 paper)

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A Dual Attention Encoding Network Using Gradient Profile Loss for Oil Spill Detection Based on SAR Images
Entropy 2022, 24(10), 1453; https://doi.org/10.3390/e24101453 - 12 Oct 2022
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Marine oil spills due to ship collisions or operational errors have caused tremendous damage to the marine environment. In order to better monitor the marine environment on a daily basis and reduce the damage and harm caused by oil pollution, we use marine [...] Read more.
Marine oil spills due to ship collisions or operational errors have caused tremendous damage to the marine environment. In order to better monitor the marine environment on a daily basis and reduce the damage and harm caused by oil pollution, we use marine image information acquired by synthetic aperture radar (SAR) and combine it with image segmentation techniques in deep learning to monitor oil spills. However, it is a significant challenge to accurately distinguish oil spill areas in original SAR images, which are characterized by high noise, blurred boundaries, and uneven intensity. Hence, we propose a dual attention encoding network (DAENet) using an encoder–decoder U-shaped architecture for identifying oil spill areas. In the encoding phase, we use the dual attention module to adaptively integrate local features with their global dependencies, thus improving the fusion feature maps of different scales. Moreover, a gradient profile (GP) loss function is used to improve the recognition accuracy of the oil spill areas’ boundary lines in the DAENet. We used the Deep-SAR oil spill (SOS) dataset with manual annotation for training, testing, and evaluation of the network, and we established a dataset containing original data from GaoFen-3 for network testing and performance evaluation. The results show that DAENet has the highest mIoU of 86.1% and the highest F1-score of 90.2% in the SOS dataset, and it has the highest mIoU of 92.3% and the highest F1-score of 95.1% in the GaoFen-3 dataset. The method proposed in this paper not only improves the detection and identification accuracy of the original SOS dataset, but also provides a more feasible and effective method for marine oil spill monitoring. Full article
(This article belongs to the Special Issue Information Network Mining and Applications)
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