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Mathematics
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Applied Network Analysis and Data Science
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Applied Network Analysis and Data Science

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3253

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

Published Papers (3 papers)

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Research

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://0-doi-org.brum.beds.ac.uk/10.3390/math12020285 - 15 Jan 2024
Viewed by 637
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)
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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://0-doi-org.brum.beds.ac.uk/10.3390/math12010164 - 04 Jan 2024
Viewed by 779
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)
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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://0-doi-org.brum.beds.ac.uk/10.3390/math11183832 - 07 Sep 2023
Viewed by 1320
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)
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