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Future Internet
Special Issues
Multi-Agent Systems for Social Media Analysis
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Multi-Agent Systems for Social Media Analysis

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Techno-Social Smart Systems".

Deadline for manuscript submissions: closed (20 May 2019) | Viewed by 4934

Special Issue Editors

Prof. Dr. Agostino Poggi

E-Mail Website
Guest Editor
Department of Engineering and Architecture, University of Parma, I-43124 Parma, Italy
Interests: distributed systems; software engineering; multi-agent systems; agent-based simulation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Michele Tomaiuolo

E-Mail Website
Guest Editor
Department of Engineering and Architecture, University of Parma, Parma, Italy
Interests: social media analysis; distributed social networking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Social media analysis is rapidly becoming a widespread tool for various applications. The motivations for this interest range from commercial marketing to the monitoring of social trends and political opinions. Often an analysis can require data coming from several sources and different analyses can require different data coming from the same sources. Usually, the execution of analyses requires the use of several tools and each analysis may use different tools, different configurations and executions. Multi-agent systems should provide suitable support to simplify and automatize the execution of complex and heterogeneous social media analysis.

This Special Issue invites original research papers on multi-agent techniques and architecture for social media analysis. Relevant topics include, but are not limited to:

  • Multi-agent architectures for social media analysis
  • Multi-agent algorithms and techniques for social media analysis
  • Multi-agent tools for social media analysis
  • Multi-agent social media analysis applications
  • Multi-agent tools for network and social media analysis
  • Multi-agent network and social media analysis applications

Prof. Agostino Poggi
Dr. Michele Tomaiuolo
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. Future Internet 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

  • Multi-agent architecture
  • Multi-agent tools
  • Coordination techniques
  • Social media analysis tools
  • Agent-based intelligent systems

Published Papers (1 paper)

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Research

16 pages, 1979 KiB  
Article
Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect
by Fei Wang, Zhenfang Zhu, Peiyu Liu and Peipei Wang
Future Internet 2019, 11(4), 95; https://doi.org/10.3390/fi11040095 - 11 Apr 2019
Cited by 6 | Viewed by 4495
Abstract
Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A [...] Read more.
Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions. Full article
(This article belongs to the Special Issue Multi-Agent Systems for Social Media Analysis)
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