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Algorithms
Special Issues
Algorithms in Online Social Networks
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Algorithms in Online Social Networks

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 June 2017)

Special Issue Editors

Dr. Pasquale De Meo

E-Mail Website
Guest Editor
Department of Ancient and Modern Civilizations, University of Messina, Messina, Italy
Interests: network science; graph mining; community detection in graphs; recommender systems; trust in virtual communities
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianguo Yao

E-Mail Website
Guest Editor
School of Software, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: distributed systems; virtualization in clouds; industrial big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Online Social Networks (OSNs), such as Facebook, Instagram, and YouTube, largely simplified and favored the production and consumption of online multimedia content, as well as the creation and consolidation of social ties among their members.

We are in urgent need for efficient algorithms to better understand how people use OSNs to disseminate the content of their interests/access relevant materials, as well as to investigate how the social structures created by OSN members is related to the content these members produce.

The open access journal Algorithms will host a Special Issue on “Algorithms in Online Social Networks”. The goal of this Special Issue is to offer a forum for exchanging new ideas about the design and implementation of efficient algorithms to analyze OSNs, as well as to propose advanced applications to benefit OSN members.

The following is a (non-exhaustive) list of topics of interest:

  • Algorithms for graph inference
  • Algorithms for managing graph streams
  • Analysis of heterogeneous, signed, and labelled networks
  • Novel data structures for graphs and social networks
  • Community detection in graphs
  • Link prediction
  • Mobile social networks
  • Privacy-preserving analyses of social networks
  • Social media mining and recommender systems
  • Reputation and trust management
  • Dynamic social networks
  • Theoretical analysis of graph algorithms and models for social networks

Dr. Pasquale De Meo
Dr. Jianguo Yao
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.

Published Papers (1 paper)

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Research

300 KiB  
Article
Properties of Vector Embeddings in Social Networks
by Fatemeh Salehi Rizi and Michael Granitzer
Algorithms 2017, 10(4), 109; https://doi.org/10.3390/a10040109 - 27 Sep 2017
Cited by 21 | Viewed by 5372
Abstract
Embedding social network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification, node clustering, link prediction and network visualization. However, the information contained in these vector embeddings remains abstract and hard to interpret. Methods [...] Read more.
Embedding social network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification, node clustering, link prediction and network visualization. However, the information contained in these vector embeddings remains abstract and hard to interpret. Methods for inspecting embeddings usually rely on visualization methods, which do not work on a larger scale and do not give concrete interpretations of vector embeddings in terms of preserved network properties (e.g., centrality or betweenness measures). In this paper, we study and investigate network properties preserved by recent random walk-based embedding procedures like node2vec, DeepWalk or LINE. We propose a method that applies learning to rank in order to relate embeddings to network centralities. We evaluate our approach with extensive experiments on real-world and artificial social networks. Experiments show that each embedding method learns different network properties. In addition, we show that our graph embeddings in combination with neural networks provide a computationally efficient way to approximate the Closeness Centrality measure in social networks. Full article
(This article belongs to the Special Issue Algorithms in Online Social Networks)
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