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Information
Special Issues
Online Social Networks and Implications
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Online Social Networks and Implications

A special issue of Information (ISSN 2078-2489).

Deadline for manuscript submissions: closed (30 June 2016) | Viewed by 4475

Special Issue Editors

Prof. Ana Paula Couto Da Silva

E-Mail Website
Guest Editor
Computer Science Dept., Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
Interests: complex network modeling; performance evaluation of computer networking
Prof. Pedro O.S Vaz de Melo

E-Mail Website
Guest Editor
Computer Science Dept., Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
Interests: knowledge discovery; data mining in complex and distributed systems

Special Issue Information

Dear Colleagues,

Online Social Networks (OSNs) are a massively successful phenomenon, used by billions of users to interact. Measuring, modelling, and analysing OSNs give the opportunity of understanding how people connect among themselves and how to improve these systems to suit people needs. This Special Issues focuses on the study of these networks. Of particular interest are studies that focus on systems, security and privacy, graphs, data management, analysis, and data mining in the context of OSNs. In summary, interested authors are encouraged to submit previously unpublished contributions in broad areas relevant to the design, analysis and development of Online Social Networks. Topics of interest include, but are not restricted to, the following:

- Systems and algorithms for social search
- Infrastructure support for social networks and systems
- Social properties in systems design
- Learnings from operational social networks
- Transient OSNs (e.g. Snapchat)
- Special purpose OSNs (e.g., Instagram, Vine)
- Academic social networks
- Measurement and analysis of social and crowdsourcing systems
- Benchmarking, modelling, performance and workload characterization
- Modelling Social Networks and behaviour
- Communities in social networks
- Information propagation and assimilation in social networks
- Data mining and machine learning in social systems
- Privacy and security in social systems
- Novel social applications and systems
- Social networks and online education
- Sentiment analysis on OSNs
- Social networks as agents of societal change

Professor Ana Paula Couto da Silva
Professor Pedro O.S Vaz de Melo
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. Information 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

  • modelling
  • analysis
  • data mining
  • graphs
  • privacy
  • social systems

Published Papers (1 paper)

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Research

261 KiB  
Article
Nearest Neighbor Search in the Metric Space of a Complex Network for Community Detection
by Suman Saha and Satya P. Ghrera
Information 2016, 7(1), 17; https://doi.org/10.3390/info7010017 - 16 Mar 2016
Cited by 6 | Viewed by 4047
Abstract
The objective of this article is to bridge the gap between two important research directions: (1) nearest neighbor search, which is a fundamental computational tool for large data analysis; and (2) complex network analysis, which deals with large real graphs but is generally [...] Read more.
The objective of this article is to bridge the gap between two important research directions: (1) nearest neighbor search, which is a fundamental computational tool for large data analysis; and (2) complex network analysis, which deals with large real graphs but is generally studied via graph theoretic analysis or spectral analysis. In this article, we have studied the nearest neighbor search problem in a complex network by the development of a suitable notion of nearness. The computation of efficient nearest neighbor search among the nodes of a complex network using the metric tree and locality sensitive hashing (LSH) are also studied and experimented. For evaluation of the proposed nearest neighbor search in a complex network, we applied it to a network community detection problem. Experiments are performed to verify the usefulness of nearness measures for the complex networks, the role of metric tree and LSH to compute fast and approximate node nearness and the the efficiency of community detection using nearest neighbor search. We observed that nearest neighbor between network nodes is a very efficient tool to explore better the community structure of the real networks. Several efficient approximation schemes are very useful for large networks, which hardly made any degradation of results, whereas they save lot of computational times, and nearest neighbor based community detection approach is very competitive in terms of efficiency and time. Full article
(This article belongs to the Special Issue Online Social Networks and Implications)
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