Special Issue "Extreme Algorithmics: Analysis of Huge, Noisy, and Dynamic Networked Data"

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 (30 April 2022) | Viewed by 909

Special Issue Editors

Prof. Dr. Davide Bilò
E-Mail Website
Guest Editor
Department of Humanities and Social Sciences, University of Sassari, 07100 Sassari SS, Italy
Interests: algorithmic game theory; network creation games; Schelling games; fault-tolerant networks; graph spanners; distance oracles; approximation algorithms for np-hard problems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maurizio Patrignani
E-Mail Website
Guest Editor
Department of Engineering, Roma Tre University, Via della Vasca Navale, 79, 00146 Rome, Italy
Interests: algorithm engineering; graph drawing; network visualization; computer networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The analysis of graphs and networks is an intriguing research field with a consolidated literature. It comprises connectivity computations, simplification through filtering, sampling and clustering, identification of hubs and authorities, visualization techniques, etc. Applications include networking, biology, economics, and sociology. However, recent challenges defy the research community. One well-known challenge is the size of real-world networks that sometimes rule out the adoption of traditional algorithms. A second challenge is bought by uncertain and noisy information that makes exact solutions questionable and calls for more flexible models. A third challenge is heterogeneous data that asks for tailored solutions. A further challenge is dynamic data, sometimes evolving over time, sometimes provided in a streaming fashion, forcing the analysis to take into account the temporal dimension.

This Special Issue is dedicated to models and algorithms for the analysis of real-world networks especially devised to cope with the mentioned challenges. Submissions should present original approaches and significant contributions and could contain methodological issues, model proposals, complexity analyses, experimental evaluations, and application-driven case studies.

Dr. Davide Bilò
Prof. Dr. Maurizio Patrignani
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 1400 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

  • Analysis of large graphs and networks 
  • Visual analysis of networked data 
  • Uncertain, noisy, and heterogeneous information 
  • Dynamic algorithms

Published Papers (1 paper)

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Research

Article
Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications
Algorithms 2022, 15(6), 209; https://doi.org/10.3390/a15060209 - 15 Jun 2022
Viewed by 517
Abstract
We propose an alternative maximum entropy approach to learning the spectra of massive graphs. In contrast to state-of-the-art Lanczos algorithm for spectral density estimation and applications thereof, our approach does not require kernel smoothing. As the choice of kernel function and associated bandwidth [...] Read more.
We propose an alternative maximum entropy approach to learning the spectra of massive graphs. In contrast to state-of-the-art Lanczos algorithm for spectral density estimation and applications thereof, our approach does not require kernel smoothing. As the choice of kernel function and associated bandwidth heavily affect the resulting output, our approach mitigates these issues. Furthermore, we prove that kernel smoothing biases the moments of the spectral density. Our approach can be seen as an information-theoretically optimal approach to learning a smooth graph spectral density, which fully respects moment information. The proposed method has a computational cost linear in the number of edges, and hence can be applied even to large networks with millions of nodes. We showcase the approach on problems of graph similarity learning and counting cluster number in the graph, where the proposed method outperforms existing iterative spectral approaches on both synthetic and real-world graphs. Full article
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