Influence Maximization Meets Efficiency and Effectiveness A Hop-Based Approach

Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-s...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) S. 64 - 71
Hauptverfasser: Tang, Jing, Tang, Xueyan, Yuan, Junsong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 31.07.2017
Schriftenreihe:ACM Conferences
Schlagworte:
Approximation algorithms
Estimation
hop-based influence estimation
Human-centered computing
Human-centered computing > Collaborative and social computing
Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms
Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms > Social networks
Human-centered computing > Human computer interaction (HCI)
Human-centered computing > Human computer interaction (HCI) > Interaction paradigms
Human-centered computing > Human computer interaction (HCI) > Interaction paradigms > Web-based interaction
influence maximization
Information systems
Information systems > Information systems applications
Information systems > Information systems applications > Data mining
Information systems > World Wide Web
Information systems > World Wide Web > Web applications
Information systems > World Wide Web > Web applications > Social networks
Integrated circuit modeling
Monte Carlo methods
Networks
Networks > Network types
Networks > Network types > Overlay and other logical network structures
Online social networks
Social networking (online)
Upper bound
influence maximization
Online social networks
hop-based influence estimation
ISBN:1450349935, 9781450349932
ISSN:2473-991X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widely as possible. However, it remains an open challenge to design fast and accurate algorithms to find solutions in large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not scalable, while other heuristic algorithms do not have any theoretical guarantee and they have been shown to produce poor solutions for quite some cases. In this paper, we propose hop-based algorithms that can easily scale to millions of nodes and billions of edges. Unlike previous heuristics, our proposed hop-based approaches can provide certain theoretical guarantees. Experimental evaluations with real OSN datasets demonstrate the efficiency and effectiveness of our algorithms.
ISBN:1450349935
9781450349932
ISSN:2473-991X
DOI:10.1145/3110025.3110041