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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 9
2689-2693

Direct causal network discovery algorithm for social media

Cai Ruichu1
Xie Yong1
Chen Wei1
Zeng Yan1
Hao Zhifeng1,2
Du Wenjun3
1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
2. School of Mathematics & Big Data, Foshan University, Foshan Guangdong 528000, China
3. School of Business Administration, Northeastern University, Shenyang 110004, China
DOI: 10.19734/j.issn.1001-3695.2019.04.0159

Abstract

Time-series causal discovery for high-dimensional networks have been increasingly significant in social media causality. However, the existing algorithms' inability to discover direct causal relations renders the results of causal network infe-rence not so accurate. Hence, this paper proposed a direct causal network discovery algorithm. It considered various factors, including the causal delay, the lag length and the conditional nodes sets, in the time-series causal model to help improve the accuracy of direct causal network inferring. Further, the method solved the difficulty of setting transfer entropy thresholds through permutation test. Experimental results demonstrate that the method outperforms the existing algorithms in causal network inferring and can conspicuously improve the accuracy of direct causal network inference on time series, which is suitable for discovering potential causal networks in social media.

Foundation Support

NSFC-广东联合基金资助项目(U1501254)
国家自然科学基金资助项目(61876043,61472089)
广东省自然科学基金资助项目(2014A030306004,2014A030308008)
广东省科技计划项目(2015B010108006,2015B010131015)
广东特支计划资助项目(2015TQ01X140)
广州市珠江科技新星资助项目(201610010101)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.04.0159
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 9
Section: Algorithm Research & Explore
Pages: 2689-2693
Serial Number: 1001-3695(2020)09-025-2689-05

Publish History

[2020-09-05] Printed Article

Cite This Article

蔡瑞初, 谢泳, 陈薇, 等. 面向社交媒体的直接因果网络发现算法 [J]. 计算机应用研究, 2020, 37 (9): 2689-2693. (Cai Ruichu, Xie Yong, Chen Wei, et al. Direct causal network discovery algorithm for social media [J]. Application Research of Computers, 2020, 37 (9): 2689-2693. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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