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Algorithms
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Algorithms for Network Analysis: Theory and Practice
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Algorithms for Network Analysis: Theory and Practice

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 4819

Special Issue Editors

Dr. Enrico Corradini

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Guest Editor
Department of Information Engineering, Polytechnic University of Marche, 60121 Ancona, Italy
Interests: social network analysis; social internetworking; source and data integration; knowledge data extraction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Domenico Ursino

E-Mail Website
Guest Editor
Department of Information Engineering, Polytechnic University of Marche, 60121 Ancona, Italy
Interests: social network analysis; databases; artificial intelligence; business intelligence; social IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the age of digital communication, social networks have become a fundamental part of our daily lives. They are not only platforms for interaction but also sources of vast amounts of data that can provide profound insights into human behavior, social dynamics, and information diffusion. The analysis of these networks, however, requires sophisticated algorithms and methods. Moreover, much of the techniques and parameters proposed for Social Network Analysis have been applied in a variety of other contexts using complex networks. 

This special issue of the MDPI journal "Algorithms" invites original research papers that explore the intersection of Algorithms and (Social) Network Analysis, both in theory and practice. We welcome contributions that propose new algorithms, methods, and models for (Social) Network Analysis, as well as empirical studies that apply these techniques to real-world social networks and, more in general, to real-world applications that can be modeled through complex networks.

Dr. Enrico Corradini
Prof. Dr. Domenico Ursino
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. Algorithms 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 1600 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

  • development and application of new algorithms for network analysis
  • theoretical aspects of network analysis algorithms
  • machine learning and data mining techniques for network analysis
  • community detection and clustering in networks
  • influence and diffusion processes in networks
  • analysis of network dynamics
  • visualization of networks
  • privacy and security issues in (social) network analysis
  • real-world examples of the application of network analysis in various fields (e.g., sociology, economics, biology, medicine, transport, etc.)

Published Papers (4 papers)

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Research

16 pages, 1240 KiB  
Article
Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph
by J. Fernando Sánchez-Rada, Raquel Vila-Rodríguez, Jesús Montes and Pedro J. Zufiria
Algorithms 2024, 17(4), 166; https://doi.org/10.3390/a17040166 - 19 Apr 2024
Viewed by 286
Abstract
Predicting vehicle mobility is crucial in domains such as ride-hailing, where the balance between offer and demand is paramount. Since city road networks can be easily represented as graphs, recent works have exploited graph neural networks (GNNs) to produce more accurate predictions on [...] Read more.
Predicting vehicle mobility is crucial in domains such as ride-hailing, where the balance between offer and demand is paramount. Since city road networks can be easily represented as graphs, recent works have exploited graph neural networks (GNNs) to produce more accurate predictions on real traffic data. However, a better understanding of the characteristics and limitations of this approach is needed. In this work, we compare several GNN aggregated mobility prediction schemes to a selection of other approaches in a very restricted and controlled simulation scenario. The city graph employed represents roads as directed edges and road intersections as nodes. Individual vehicle mobility is modeled as transitions between nodes in the graph. A time series of aggregated mobility is computed by counting vehicles in each node at any given time. Three main approaches are employed to construct the aggregated mobility predictors. First, the behavior of the moving individuals is assumed to follow a Markov chain (MC) model whose transition matrix is inferred via a least squares estimation procedure; the recurrent application of this MC provides the aggregated mobility prediction values. Second, a multilayer perceptron (MLP) is trained so that—given the node occupation at a given time—it can recursively provide predictions for the next values of the time series. Third, we train a GNN (according to the city graph) with the time series data via a supervised learning formulation that computes—through an embedding construction for each node in the graph—the aggregated mobility predictions. Some mobility patterns are simulated in the city to generate different time series for testing purposes. The proposed schemes are comparatively assessed compared to different baseline prediction procedures. The comparison illustrates several limitations of the GNN approaches in the selected scenario and uncovers future lines of investigation. Full article
(This article belongs to the Special Issue Algorithms for Network Analysis: Theory and Practice)
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11 pages, 404 KiB  
Article
PDASTSGAT: An STSGAT-Based Multipath Data Scheduling Algorithm
by Sen Xue, Chengyu Wu, Jing Han and Ao Zhan
Algorithms 2024, 17(4), 145; https://doi.org/10.3390/a17040145 - 30 Mar 2024
Viewed by 574
Abstract
How to select the transmitting path in MPTCP scheduling is an important but open problem. This paper proposes an intelligent data scheduling algorithm using spatiotemporal synchronous graph attention neural networks to improve MPTCP scheduling. By exploiting the spatiotemporal correlations in the data transmission [...] Read more.
How to select the transmitting path in MPTCP scheduling is an important but open problem. This paper proposes an intelligent data scheduling algorithm using spatiotemporal synchronous graph attention neural networks to improve MPTCP scheduling. By exploiting the spatiotemporal correlations in the data transmission process and incorporating graph self-attention mechanisms, the algorithm can quickly select the optimal transmission path and ensure fairness among similar links. Through simulations in NS3, the algorithm achieves a throughput gain of 7.9% compared to the PDAA3C algorithm and demonstrates improved packet transmission performance. Full article
(This article belongs to the Special Issue Algorithms for Network Analysis: Theory and Practice)
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20 pages, 552 KiB  
Article
An Attention-Based Method for the Minimum Vertex Cover Problem on Complex Networks
by Giorgio Lazzarinetti, Riccardo Dondi, Sara Manzoni and Italo Zoppis
Algorithms 2024, 17(2), 72; https://doi.org/10.3390/a17020072 - 06 Feb 2024
Viewed by 1475
Abstract
Solving combinatorial problems on complex networks represents a primary issue which, on a large scale, requires the use of heuristics and approximate algorithms. Recently, neural methods have been proposed in this context to find feasible solutions for relevant computational problems over graphs. However, [...] Read more.
Solving combinatorial problems on complex networks represents a primary issue which, on a large scale, requires the use of heuristics and approximate algorithms. Recently, neural methods have been proposed in this context to find feasible solutions for relevant computational problems over graphs. However, such methods have some drawbacks: (1) they use the same neural architecture for different combinatorial problems without introducing customizations that reflects the specificity of each problem; (2) they only use a nodes local information to compute the solution; (3) they do not take advantage of common heuristics or exact algorithms. Following this interest, in this research we address these three main points by designing a customized attention-based mechanism that uses both local and global information from the adjacency matrix to find approximate solutions for the Minimum Vertex Cover Problem. We evaluate our proposal with respect to a fast two-factor approximation algorithm and a widely adopted state-of-the-art heuristic both on synthetically generated instances and on benchmark graphs with different scales. Experimental results demonstrate that, on the one hand, the proposed methodology is able to outperform both the two-factor approximation algorithm and the heuristic on the test datasets, scaling even better than the heuristic with harder instances and, on the other hand, is able to provide a representation of the nodes which reflects the combinatorial structure of the problem. Full article
(This article belongs to the Special Issue Algorithms for Network Analysis: Theory and Practice)
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20 pages, 837 KiB  
Article
Group Dynamics in Memory-Enhanced Ant Colonies: The Influence of Colony Division on a Maze Navigation Problem
by Claudia Cavallaro, Carolina Crespi, Vincenzo Cutello, Mario Pavone and Francesco Zito
Algorithms 2024, 17(2), 63; https://doi.org/10.3390/a17020063 - 01 Feb 2024
Viewed by 1629
Abstract
This paper introduces an agent-based model grounded in the ACO algorithm to investigate the impact of partitioning ant colonies on algorithmic performance. The exploration focuses on understanding the roles of group size and number within a multi-objective optimization context. The model consists of [...] Read more.
This paper introduces an agent-based model grounded in the ACO algorithm to investigate the impact of partitioning ant colonies on algorithmic performance. The exploration focuses on understanding the roles of group size and number within a multi-objective optimization context. The model consists of a colony of memory-enhanced ants (ME-ANTS) which, starting from a given position, must collaboratively discover the optimal path to the exit point within a grid network. The colony can be divided into groups of different sizes and its objectives are maximizing the number of ants that exit the grid while minimizing path costs. Three distinct analyses were conducted: an overall analysis assessing colony performance across different-sized groups, a group analysis examining the performance of each partitioned group, and a pheromone distribution analysis discerning correlations between temporal pheromone distribution and ant navigation. From the results, a dynamic correlation emerged between the degree of colony partitioning and solution quality within the ACO algorithm framework. Full article
(This article belongs to the Special Issue Algorithms for Network Analysis: Theory and Practice)
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