Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
2.3
Journals
Mathematics
Special Issues
Applied Network Analysis and Data Science
share announcement

Applied Network Analysis and Data Science

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 11829

Special Issue Editors

Dr. Nazim Choudhury

E-Mail Website
Guest Editor
Department of Computer Science, University of Wisconsin-Green Bay, Green Bay, WI 54311, USA
Interests: social network analysis; human-computer interactions; machine learning
Dr. Matloob Khushi

E-Mail Website
Guest Editor
Department of Computer Science, Brunel University London, Uxbridge UB8 3PH, UK
Interests: AI & data mining; data science; bioinformatics

Special Issue Information

Dear Colleagues,

Network and data sciences are indispensable in understanding the patterns of interconnections in a wide range of complex social, biological, and physical systems. As a domain that intertwines both network and data science, “Network Data Science” refers to the use of data science algorithms, techniques, and tools in the modeling and analysis of network data.

Our everyday lives are pervaded by several social networks. Using these networks, we follow, connect, and share with family, colleagues, friends, and even unknowns (e.g., Facebook), search for jobs and opportunities (e.g., LinkedIn), evaluate and recommend products and services (e.g., Yelp), work or establish collaborative projects (e.g., Github), or communicate and keep ourselves conversant with the news and topics of our interests (e.g., Reddit). The wealth of network data available from these platforms provides us with the opportunity to explore complex social interactions and human dynamics and different social phenomena including social structure evolution, communities, network spread, and the dynamics of changes in networks.

This Special Issue hopes to serve as a solid international exchange platform for both network and data scientists and invites both network science fundamental research and application-driven research on network link analysis, centrality and prestige, modularity, communities, biases and manipulation, visualization, dynamics of information, opinion mining, and models of information diffusion. It welcomes the application of network analysis in different social, biological, and physical networks.

Dr. Nazim Choudhury
Dr. Matloob Khushi
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. Mathematics 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 2600 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

  • link mining
  • network communities
  • information diffusion
  • network manipulation
  • network visualization
  • machine learning
  • artificial intelligence
  • statistical learning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 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

18 pages, 2701 KiB  
Article
HyperCLR: A Personalized Sequential Recommendation Algorithm Based on Hypergraph and Contrastive Learning
by Ruiqi Zhang, Haitao Wang and Jianfeng He
Mathematics 2024, 12(18), 2887; https://doi.org/10.3390/math12182887 - 16 Sep 2024
Cited by 1 | Viewed by 1086
Abstract
Sequential recommendations aim to predict users’ next interactions by modeling their interaction sequences. Most existing work concentrates on user preferences within these sequences, overlooking the complex item relationships across sequences. Additionally, these studies often fail to address the diversity of user interests, thus [...] Read more.
Sequential recommendations aim to predict users’ next interactions by modeling their interaction sequences. Most existing work concentrates on user preferences within these sequences, overlooking the complex item relationships across sequences. Additionally, these studies often fail to address the diversity of user interests, thus not capturing their varied latent preferences effectively. To tackle these problems, this paper develops a novel recommendation algorithm based on hypergraphs and contrastive learning named HyperCLR. It dynamically incorporates the time and location embeddings of items to model high-order relationships in user preferences. Moreover, we developed a graph construction approach named IFDG, which utilizes global item visit frequencies and spatial distances to discern item relevancy. By sampling subgraphs from IFDG, HyperCLR can align the representations of identical interaction sequences closely while distinguishing them from the broader global context on IFDG. This approach enhances the accuracy of sequential recommendations. Furthermore, a gating mechanism is designed to tailor the global context information to individual user preferences. Extensive experiments on Taobao, Books and Games datasets have shown that HyperCLR consistently surpasses baselines, demonstrating the effectiveness of the method. In particular, in comparison to the best baseline methods, HyperCLR demonstrated a 29.1% improvement in performance on the Taobao dataset. Full article
(This article belongs to the Special Issue Applied Network Analysis and Data Science)
►▼ Show Figures

Figure 1

16 pages, 3150 KiB  
Article
Blin: A Multi-Task Sequence Recommendation Based on Bidirectional KL-Divergence and Linear Attention
by Yanfeng Bai, Haitao Wang and Jianfeng He
Mathematics 2024, 12(15), 2391; https://doi.org/10.3390/math12152391 - 31 Jul 2024
Viewed by 1258
Abstract
Sequence recommendation is a prominent research area within recommender systems, focused on predicting items that users may be interested in by modeling their historical interaction sequences. However, due to data sparsity, user interaction sequences in sequence recommendation are typically short. A common approach [...] Read more.
Sequence recommendation is a prominent research area within recommender systems, focused on predicting items that users may be interested in by modeling their historical interaction sequences. However, due to data sparsity, user interaction sequences in sequence recommendation are typically short. A common approach to address this issue is filling sequences with zero values, significantly reducing the effective utilization of input space. Furthermore, traditional sequence recommendation methods based on self-attention mechanisms exhibit quadratic complexity with respect to sequence length. These issues affect the performance of recommendation algorithms. To tackle these challenges, we propose a multi-task sequence recommendation model, Blin, which integrates bidirectional KL divergence and linear attention. Blin abandons the conventional zero-padding strategy, opting instead for random repeat padding to enhance sequence data. Additionally, bidirectional KL divergence loss is introduced as an auxiliary task to regularize the probability distributions obtained from different sequence representations. To improve the computational efficiency compared to traditional attention mechanisms, a linear attention mechanism is employed during sequence encoding, significantly reducing the computational complexity while preserving the learning capacity of traditional attention. Experimental results on multiple public datasets demonstrate the effectiveness of the proposed model. Full article
(This article belongs to the Special Issue Applied Network Analysis and Data Science)
►▼ Show Figures

Figure 1

24 pages, 5500 KiB  
Article
Community-Aware Evolution Similarity for Link Prediction in Dynamic Social Networks
by Nazim Choudhury
Mathematics 2024, 12(2), 285; https://doi.org/10.3390/math12020285 - 15 Jan 2024
Cited by 4 | Viewed by 1648
Abstract
The link prediction problem is a time-evolving model in network science that has simultaneously abetted myriad applications and experienced extensive methodological improvement. Inferring the possibility of emerging links in dynamic social networks, also known as the dynamic link prediction task, is complex and [...] Read more.
The link prediction problem is a time-evolving model in network science that has simultaneously abetted myriad applications and experienced extensive methodological improvement. Inferring the possibility of emerging links in dynamic social networks, also known as the dynamic link prediction task, is complex and challenging. In contrast to the link prediction in cross-sectional networks, dynamic link prediction methods need to cater to the actor-level temporal changes and associated evolutionary information regarding their micro- (i.e., link formation/deletion) and mesoscale (i.e., community formation) network structure. With the advent of abundant community detection algorithms, the research community has examined community-aware link prediction strategies in static networks. However, the same task in dynamic networks where, apart from the actors and links among them, their community pattern is also dynamic, is yet to be explored. Evolutionary community-aware information, including the associated link structure and temporal neighborhood changes, can effectively be mined to build dynamic similarity metrics for dynamic link prediction. This study aims to develop and integrate such dynamic features with machine learning algorithms for link prediction tasks in dynamic social networks. It also compares the performances of these features against well-known similarity metrics (i.e., ResourceAllocation) for static networks and a time series-based link prediction strategy in dynamic networks. These proposed features achieved high-performance scores, representing them as prospective candidates for both dynamic link prediction tasks and modeling the network growth. Full article
(This article belongs to the Special Issue Applied Network Analysis and Data Science)
►▼ Show Figures

Figure 1

21 pages, 682 KiB  
Article
GSRec: A Graph-Sequence Recommendation System Based on Reverse-Order Graph and User Embedding
by Xulin Ma, Jiajia Tan, Linan Zhu, Xiaoran Yan and Xiangjie Kong
Mathematics 2024, 12(1), 164; https://doi.org/10.3390/math12010164 - 4 Jan 2024
Cited by 2 | Viewed by 3722
Abstract
At present, sequence-based models have various applications in recommendation systems; these models recommend the interested items of the user according to the user’s behavioral sequence. However, sequence-based models have a limitation of length. When the length of the user’s behavioral sequence exceeds the [...] Read more.
At present, sequence-based models have various applications in recommendation systems; these models recommend the interested items of the user according to the user’s behavioral sequence. However, sequence-based models have a limitation of length. When the length of the user’s behavioral sequence exceeds the limitation of the model, the model cannot take advantage of the complete behavioral sequence of the user and cannot know the user’s holistic interests. The accuracy of the model then goes down. Meanwhile, sequence-based models only pay attention to the sequential signals of the data but do not pay attention to the spatial signals of the data, which will also affect the model’s accuracy. This paper proposes a graph sequence-based model called GSRec that combines Graph Convolutional Network (GCN) and Transformer to solve these problems. In the GCN part we designed a reverse-order graph, and in the Transformer part we introduced the user embedding. The reverse-order graph and the user embedding can make the combination of GCN and Transformer more efficient. Experiments on six datasets show that GSRec outperforms the current state-of-the-art (SOTA) models. Full article
(This article belongs to the Special Issue Applied Network Analysis and Data Science)
►▼ Show Figures

Figure 1

13 pages, 828 KiB  
Article
Distance Correlation Market Graph: The Case of S&P500 Stocks
by Samuel Ugwu, Pierre Miasnikof and Yuri Lawryshyn
Mathematics 2023, 11(18), 3832; https://doi.org/10.3390/math11183832 - 7 Sep 2023
Cited by 1 | Viewed by 3110
Abstract
This study investigates the use of a novel market graph model for equity markets. Our graph model is built on distance correlation instead of the traditional Pearson correlation. We apply it to the study of S&P500 stocks from January 2015 to December 2022. [...] Read more.
This study investigates the use of a novel market graph model for equity markets. Our graph model is built on distance correlation instead of the traditional Pearson correlation. We apply it to the study of S&P500 stocks from January 2015 to December 2022. We also compare our market graphs to the traditional market graphs in the literature, those built using Pearson correlation. To further the comparison, we also build graphs using Spearman rank correlation. Our comparisons reveal that non-linear relationships in stock returns are not captured by either Pearson correlation or Spearman rank correlation. We observe that distance correlation is a robust measure for detecting complex relationships in S&P500 stock returns. Networks built on distance correlation networks, are shown to be more responsive to market conditions during turbulent periods such as the COVID crash period. Full article
(This article belongs to the Special Issue Applied Network Analysis and Data Science)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop