Aims and ScopeThis special issue focuses on Computational Aspects of Network Science. Network Science isan active area of research with significant applications in diverse scientific fields. It is very hard to detect an application that does not require a form of a graph behind the scenes. In fact, manyalgorithmic techniques transform the problem to a graph-theoretic problem in order to deliverelegant and more efficient solutions. We are inviting researchers and practitioners working withnetworks and graphs to submit their work to this special issue, aiming at a collection of papersthat reflect the current trends and developments in the area.
About the JournalWorld Wide Web: Internet and Web Information Systems (WWW), published by Springer, is aninternational, archival, peer-reviewed journal that covers all aspects of the Web, including issuesrelated to architectures, applications, Internet and Web information systems, and communities.It provides in-depth coverage of the most recent developments in the Web, enabling readers tokeep up-to-date with this dynamically changing technology. The journal also focuses on alldatabase- and information-system topics that relate to the Internet and the Web, particularly onways to model, design, develop, integrate, and manage these systems. For more informationplease visit the journal’s home page: https://www.springer.com/journal/11280
Main Topics of InterestWe invite authors from academia and industry to submit their original research to present thelatest progress for current development or future goals in this field. Topics of interest include, butare not limited to:
- Big Data Algorithms for Graphs: Parallel and distributed techniques, streaming graph processing, graph sparsification, data reduction, sampling, sketching.
- Web: automatic discovery and analysis of Web social networks, link topology and site hierarchy, web communities, web mining algorithms.
- Network Representation Learning: graph embeddings, scalable node and graph embedding techniques, hashed-based network embeddings.
- Graph Mining, Evolution and Growth: community detection/search, information diffusion, influence maximization, topology of real networks.
- Graph Databases: NoSQL, RDF, query languages, compressing, indexing, mining, parallel and distributed processing.
- Recommendation: link prediction/recommendation, evolution of social networks, classification in social recommenders.
- Search in Networks: web page ranking informed by social media, search algorithms on social networks, collaborative filtering.
- Security: anomaly detection, data protection inside communities, crime data mining and network analysis, modelling trust and reputation.
- Network Geography: geographical clusters, networks and innovation, social geography, international collaborations in e-social network.
- Advertisement Models: economics of social network discovery, social advertising, use of social networks for marketing
Submission GuidelinesAuthors should select the Computational Aspects of Network Science Special Issue and follow the “Instructions for Authors” at theSubmission Guidelines WWW webpage:https://www.springer.com/journal/11280/submission-guidelines
Important DatesSubmission system opening: December 1st, 2020Submission system closing: March 1st, 2021First review notification: April 1st, 2021Resubmission of revised manuscripts: May 1st, 2021Final notification due: June 15th, 2021Camera ready deadline: June 30th, 2021
Guest EditorsApostolos Papadopoulos, Aristotle University of Thessaloniki, GreeceRichard Chbeir, University of Pau and Pays Adour, FranceJan Platos, Technical University of Ostrava, Czech RepublicVaclav Snasel, Technical University of Ostrava, Czech Republic
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.
All papers will be reviewed following standard reviewing procedures for the Journal.
Papers must be prepared in accordance with the Journal guidelines: www.springer.com/11280
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Guest Editor Bios
- Apostolos Papadopoulos is Associate Professor of Computer Science at the School of Informatics of Aristotle University of Thessaloniki (AUTH). He received his 5-year Diploma Degree in Computer Engineering and Informatics from the University of Patras, and his Ph.D. Degree in Informatics from the School of Informatics (AUTH). His research interests include Data Management, Data Mining and Big Data Analytics. He has served as a track co-chair of ACM SAC DTTA (Database Technologies Techniques and Applications) Track from 2005 until now, as well as a PC member in several International Conferences related to Data Management and Data Mining. He has co-presented four tutorials in ASONAM 2015, EDBT/ICDT 2016, ICDM 2016 and ECML/PKDD 2017 on the “Core Decomposition of Networks”. Based on Google Scholar, he has received more than 3200 citations in his research work.
- Richard Chbeir received his PhD in Computer Science from the University of INSA DE LYONFRANCE in 2001 and then his Habilitation degree in 2010 from the University of Bourgogne. He is currently a Full Professor and head of the Computer Science laboratory (LIUPPA) of Univ. Pau & Pays Adour located in Anglet-France. His current research interests are in the areas of Information extraction/semantics and digital ecosystems. Richard Chbeir has published in international journals, books, and conferences, and has served on the program committees of several international conferences. He is currently the Chair of the French Chapter ACM SIGAPP and OpenCEMS.
- Jan Platos received the Ph.D. degree in computer science from the VSB-Technical University of Ostrava, Ostrava, Czech Republic, in 2006. He became an Associate Professor in computer science in 2014. Since 2017, he has been the Head of the Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Techincal University of Ostrava. He has coauthored more than 200 scientific articles published in proceedings and journals, and his citation report consists of 338 citations and H-index of 10 on the Web of Science, 800 citations and H-index of 14 on Scopus, and 1213 citations and H-index of 17 on Google Scholar. His primary fields of interest are text processing, data compression, bioinspired algorithms, information retrieval, data mining, data structures, and data prediction.
- Vaclav Snasel received a master’s degree in numerical mathematics from the Faculty of Science, Palacky University, Olomouc, Czech Republic, in 1981, and the Ph.D. degree in algebra and number theory from Masaryk University, Brno, Czech Republic, in 1991. He is currently a Full Professor with the VŠBTechnical University of Ostrava, Ostrava, Czech Republic. His research and development experience includes more than 30 years in the industry and academia. He works in a multidisciplinary environment involving artificial intelligence, social networks, conceptual lattice, information retrieval, semantic web, knowledge management, data compression, machine intelligence, and nature and bioinspired computing applied to various real-world problems. He has authored or co-authored several refereed journal/conference papers, books, and book chapters. Dr. Snášel is the Chair of the IEEE International Conference on Systems, Man, and Cybernetics, Czechoslovak Chapter. He also served as an Editor/Guest Editor for several journals, such as Engineering Applications of Artificial Intelligence (Elsevier), Neurocomputing (Elsevier), and Journal of Applied Logic (Elsevier).