Next Article in Journal
Conditional Lie–Bäcklund Symmetries and Functionally Generalized Separation of Variables to Quasi-Linear Diffusion Equations with Source
Next Article in Special Issue
Learning Context-Aware Outfit Recommendation
Previous Article in Journal
New Weighted Opial-Type Inequalities on Time Scales for Convex Functions
Previous Article in Special Issue
A New LSB Attack on Special-Structured RSA Primes
Open AccessFeature PaperArticle

A Two-Tier Partition Algorithm for the Optimization of the Large-Scale Simulation of Information Diffusion in Social Networks

1
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
2
Department of Information Management and Information Systems, School of Management, Fudan University, Shanghai 200433, China
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(5), 843; https://doi.org/10.3390/sym12050843
Received: 26 April 2020 / Revised: 13 May 2020 / Accepted: 14 May 2020 / Published: 21 May 2020
(This article belongs to the Special Issue Recent Advances in Social Data and Artificial Intelligence 2019)
As online social networks play a more and more important role in public opinion, the large-scale simulation of social networks has been focused on by many scientists from sociology, communication, informatics, and so on. It is a good way to study real information diffusion in a symmetrical simulation world by agent-based modeling and simulation (ABMS), which is considered an effective solution by scholars from computational sociology. However, on the one hand, classical ABMS tools such as NetLogo cannot support the simulation of more than thousands of agents. On the other hand, big data platforms such as Hadoop and Spark used to study big datasets do not provide optimization for the simulation of large-scale social networks. A two-tier partition algorithm for the optimization of large-scale simulation of social networks is proposed in this paper. First, the simulation kernel of ABMS for information diffusion is implemented based on the Spark platform. Both the data structure and the scheduling mechanism are implemented by Resilient Distributed Data (RDD) to simulate the millions of agents. Second, a two-tier partition algorithm is implemented by community detection and graph cut. Community detection is used to find the partition of high interactions in the social network. A graph cut is used to achieve the goal of load balance. Finally, with the support of the dataset recorded from Twitter, a series of experiments are used to testify the performance of the two-tier partition algorithm in both the communication cost and load balance. View Full-Text
Keywords: social network simulation; ABMS; Spark; two-tier partition algorithm social network simulation; ABMS; Spark; two-tier partition algorithm
Show Figures

Figure 1

MDPI and ACS Style

Chen, B.; Chen, H.; Ning, D.; Zhu, M.; Ai, C.; Qiu, X.; Dai, W. A Two-Tier Partition Algorithm for the Optimization of the Large-Scale Simulation of Information Diffusion in Social Networks. Symmetry 2020, 12, 843.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop