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Mathematics
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Deep Representation Learning for Social Network Analysis
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Deep Representation Learning for Social Network Analysis

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

Deadline for manuscript submissions: 31 January 2025 | Viewed by 1129

Special Issue Editors

Dr. Qiang He

E-Mail Website
Guest Editor
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110001, China
Interests: social networks and social applications; computational-data-mining reinforcement learning; multi-agent systems and autonomous agents; opinion dynamics
Dr. Jianxiong Guo

E-Mail Website
Guest Editor
Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai 519087, China.
Interests: social networks and social computing; IoT and edge computing; online optimization; combinatorial optimization; machine learning

Special Issue Information

Dear Colleagues,

I invite you to submit your latest research in the area of mathematical optimization to this Special Issue titled “Deep Representation Learning for Social Network Analysis” in the journal Mathematics. Social network analysis arises in all fields in the real world and has immense importance. Deep representation learning, as an emerging method, provides new ideas and solutions for social network analysis. High-quality papers that address both theoretical and practical issues in the area of deep representation learning for social network analysis and submissions that present new theoretical results, models, and algorithms, as well as new applications, are welcome. Potential topics include, but are not limited to, social network structure analysis, the influence of users’ social network nodes, community discovery, dynamic social network analysis, user behavior, link prediction, social recommendation systems, information dissemination, anomaly detection, data mining, and other research problems.

Dr. Qiang He
Dr. Jianxiong Guo
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

  • social network structure analysis
  • the influence of users’ social network nodes
  • community discovery
  • dynamic social network analysis
  • user behavior
  • link prediction
  • social recommendation systems
  • information dissemination in social networks
  • anomaly detection
  • social network data mining
  • predicting future development trends in social network data
  • node-embedded learning
  • influence maximization
  • infectious disease analysis
  • malicious rumor control
  • rumor detection
  • healthcare applications
  • machine learning

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Published Papers (2 papers)

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Research

19 pages, 609 KiB  
Article
An Attribute Graph Embedding Algorithm for Sensing Topological and Attribute Influence
by Dongming Chen, Shuyue Zhang, Yumeng Zhao, Mingzhao Xie and Dongqi Wang
Mathematics 2024, 12(23), 3644; https://doi.org/10.3390/math12233644 - 21 Nov 2024
Viewed by 238
Abstract
The unsupervised attribute graph embedding technique aims to learn low-dimensional node embedding using neighborhood topology and attribute information under unlabeled data. Current unsupervised models are mostly based on graph self-encoders, but full-batch training limits the scalability of the model and ignores attribute integrity [...] Read more.
The unsupervised attribute graph embedding technique aims to learn low-dimensional node embedding using neighborhood topology and attribute information under unlabeled data. Current unsupervised models are mostly based on graph self-encoders, but full-batch training limits the scalability of the model and ignores attribute integrity when reconstructing the topology. In order to solve the above problems while considering the unsupervised learning of the model and full use of node information, this paper proposes a graph neural network architecture based on a graph self-encoder to capture the nonlinearity of the attribute graph data, and an attribute graph embedding algorithm that explicitly models the influence of neighborhood information using a multi-level attention mechanism. Specifically, the proposed algorithm fuses topology information and attribute information using a lightweight sampling strategy, constructs an unbiased graph self-encoder on the sampled graph, implements topology aggregation and attribute aggregation, respectively, models the correlation between topology embedding and attribute embedding, and considers multi-level loss terms. Full article
(This article belongs to the Special Issue Deep Representation Learning for Social Network Analysis)
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18 pages, 10246 KiB  
Article
Hypergraph-Based Influence Maximization in Online Social Networks
by Chuangchuang Zhang, Wenlin Cheng, Fuliang Li and Xingwei Wang
Mathematics 2024, 12(17), 2769; https://doi.org/10.3390/math12172769 - 7 Sep 2024
Cited by 1 | Viewed by 607
Abstract
Influence maximization in online social networks is used to select a set of influential seed nodes to maximize the influence spread under a given diffusion model. However, most existing proposals have huge computational costs and only consider the dyadic influence relationship between two [...] Read more.
Influence maximization in online social networks is used to select a set of influential seed nodes to maximize the influence spread under a given diffusion model. However, most existing proposals have huge computational costs and only consider the dyadic influence relationship between two nodes, ignoring the higher-order influence relationships among multiple nodes. It limits the applicability and accuracy of existing influence diffusion models in real complex online social networks. To this end, in this paper, we present a novel information diffusion model by introducing hypergraph theory to determine the most influential nodes by jointly considering adjacent influence and higher-order influence relationships to improve diffusion efficiency. We mathematically formulate the influence maximization problem under higher-order influence relationships in online social networks. We further propose a hypergraph sampling greedy algorithm (HSGA) to effectively select the most influential seed nodes. In the HSGA, a random walk-based influence diffusion method and a Monte Carlo-based influence approximation method are devised to achieve fast approximation and calculation of node influences. We conduct simulation experiments on six real datasets for performance evaluations. Simulation results demonstrate the effectiveness and efficiency of the HSGA, and the HSGA has a lower computational cost and higher seed selection accuracy than comparison mechanisms. Full article
(This article belongs to the Special Issue Deep Representation Learning for Social Network Analysis)
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