Algorithmic Game Theory and Graph Mining

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: closed (1 December 2022) | Viewed by 5832

Special Issue Editors


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Guest Editor
Department of Economic Studies, University of Chieti-Pescara, Via dei Vestini, 31, 66100 Chieti, CH, Italy
Interests: algorithms and complexity; algorithmic game theory; multi-agent systems; economic and computation
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Guest Editor
Faculty of Computer Science, University of Vienna, 1090 Vienna, Austria
Interests: network analysis; graph algorithms; algorithms and complexity; algorithmic game theory; knowledge discovery; data mining; clustering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Algorithmic Game Theory combines algorithmic thinking with game-theoretic concepts. It is inherently interdisciplinary, sitting at the interface of mathematics, economics, computer science, and operations research.

Graph mining is a process in which the mining techniques are used for identifying the most important characteristics and properties of graphs. It is a very relevant field of research with applications in many different domains, from business to medicine.

This special issue solicits papers addressing original research on foundations, theory, development, analysis, and applications of algorithmic game theory and graph mining. Topics of interest include (non-exhaustive list): solution concepts in game theory; efficiency of stable outcomes; complexity classes in game theory; algorithmic mechanism design; coalitions, coordination, and collective action; auction design and analysis; economic paradigms; cooperative and non-cooperative algorithmic game theory; social choice and voting; network games and graph-theoretic aspects of social networks; supervised and unsupervised learning; clustering, classification and link recommendation; diffusion dynamics on networks; analysis of time-evolving graphs.

Prof. Dr. Gianpiero Monaco
Dr. Yllka Velaj
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

  •  solution concepts in game theory
  •  efficiency of stable outcomes
  •  complexity classes in game theory
  •  algorithmic mechanism design
  •  coalitions, coordination, and collective action
  •  auction design and analysis
  •  economic paradigms
  •  cooperative and non-cooperative algorithmic game theory
  •  social choice and voting
  •  network games and graph-theoretic aspects of social networks
  •  supervised and unsupervised learning
  •  clustering, classification and link recommendation
  •  diffusion dynamics on networks
  •  analysis of time-evolving graphs

Published Papers (2 papers)

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Research

25 pages, 701 KiB  
Article
Evaluation, Comparison and Monitoring of Multiparameter Systems by Unified Graphic Visualization of Activity Method on the Example of Learning Process
by Viktor Uglev and Oleg Sychev
Algorithms 2022, 15(12), 468; https://doi.org/10.3390/a15120468 - 9 Dec 2022
Cited by 2 | Viewed by 2797
Abstract
The article discusses the problem of visualization of complex multiparameter systems, defined by datasets on their structure, functional structure, and activity in the form of complex graphs and transition of traditional representation of the data acquired by graph mining to a compact image [...] Read more.
The article discusses the problem of visualization of complex multiparameter systems, defined by datasets on their structure, functional structure, and activity in the form of complex graphs and transition of traditional representation of the data acquired by graph mining to a compact image built by pictographic methods. In these situations, we propose using the Unified Graphic Visualization of Activity (UGVA) method for data concentration and structuring. The UGVA method allows coding in an anthropomorphic image of elements of graphs with data on structural and functional features of systems and overlaying these images with the data on the system’s activity using coloring and artifacts. The image can be composed in different ways: it can include the zone of integral evaluation parameters, segmented data axes of five types, and four types of symmetry. We describe the method of creating UGVA images, which consists of 13 stages: the parametric model is represented as a structural image that is converted to a basic image that is then detailed into the particular image by defining geometric parameters of the primitives and to the individualized image with the data about a particular object. We show how the individualized image can be overlaid with the operative data as color coding and artifacts and describe the principles of interpreting UGVA images. This allows solving tasks of evaluation, comparison, and monitoring of complex multiparameter systems by showing the decision-maker an anthropomorphic image instead of the graph. We describe a case study of using the UGVA method for visualization of data about an educational process: curricula and graduate students, including the data mined from the university’s learning management system at the Siberian Federal University for students majoring in “informatics and computing”. The case study demonstrates all stages of image synthesis and examples of their interpretation for situation assessment, monitoring, and comparison of students and curricula. It allowed for finding problematic moments in learning for individual students and their entire group by analyzing the development of their competence profiles and formulating recommendations for further learning. The effectiveness of the resulting images is compared to the other approaches: elastic maps and Chernoff faces. We discuss using graph mining to generate learning problems in order to lessen the workload of gathering raw data for the UGVA method and provide general recommendations for using the UGVA method based on our experience of supporting decision making. Full article
(This article belongs to the Special Issue Algorithmic Game Theory and Graph Mining)
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11 pages, 914 KiB  
Article
Bargaining Power in Cooperative Resource Allocations Games
by Kaveh Madani, Faraz Farhidi and Sona Gholizadeh
Algorithms 2022, 15(12), 445; https://doi.org/10.3390/a15120445 - 25 Nov 2022
Cited by 2 | Viewed by 1776
Abstract
Cooperative game theory provides an appropriate framework to assess the likelihood of conflict resolution, encourage cooperation among parties, and determine each party’s share in resource sharing conflicts. In calculating the fair and efficient allocation of the incremental benefits of cooperation, cooperative game theory [...] Read more.
Cooperative game theory provides an appropriate framework to assess the likelihood of conflict resolution, encourage cooperation among parties, and determine each party’s share in resource sharing conflicts. In calculating the fair and efficient allocation of the incremental benefits of cooperation, cooperative game theory methods often do not consider the exogenous bargaining powers of the players based on factors, that are external to the game, such as their political, economic, and military powers. This study reformulates three well-known cooperative game theory methods, namely, Nash-Harsanyi, Shapley, and Nucleolus, to account for the exogenous bargaining powers of the players in cooperative games. Using the Caspian Sea international conflict as an example, this paper shows how the negotiators’ exogenous bargaining power can change the outcome of resource sharing games. The proposed weighted cooperative game theory approach can help determine practical resolutions for real-world conflicts in which the exogenous powers of players can have a significant influence on the outcome of negotiations. Full article
(This article belongs to the Special Issue Algorithmic Game Theory and Graph Mining)
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