Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 6
1694-1699,1748

Positive unlabeled graph learning based on collective inference

Chen Hang
Liang Chunquan
Wang Zi
Zhao Hang
College of Information Engineering, Northwest A&F University, Yangling Shaanxi 712100, China
DOI: 10.19734/j.issn.1001-3695.2021.11.0604

Abstract

Most existing positive-unlabeled(PU) graph learning methods exact only node representations to infer node labels independently. This paper proposed a collective inference based method that exploited the correlations among nodes to assist in classification of unlabeled nodes. Firstly, it used the personalized PageRank algorithm to approximate correlation degrees between each node and observed positive nodes as a whole(positive correlation degree, PCD for short). Then, it built a local classifier to predict labels of unknown nodes by combining PCD with node representations that were captured via a graph neural network(GNN), and constructed a relational classifier to collectively update labels of unknown nodes by combining PCD with local label dependency of nodes that were exacted via another GNN. Furtherly, it exploited the Markov GNN(GMNN) framework to train these two classifiers, alternately and iteratively, to form a multi-hop collective inference procedure. Besides, it proposed a mixed non-negative unbiased risk estimator for the two classifiers to estimate empirical loss with only positive and unlabeled nodes. Finally, either of them could predict labels of unknown nodes. Experimental results on real-life datasets show that the proposed method remarkably outperforms the state-of-the-art approaches in identifying both single-class target concept and multiple-classes-merged target concept, and performs quite robust against to error of the prior of positive nodes.

Foundation Support

国家自然科学基金资助项目(61402375)
陕西省重点研发计划资助项目(2019ZDLNY07-02-01)
西北农林科技大学中央高校基本科研业务费专项基金资助项目(2452019065)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.11.0604
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 6
Section: Algorithm Research & Explore
Pages: 1694-1699,1748
Serial Number: 1001-3695(2022)06-016-1694-06

Publish History

[2022-01-17] Accepted Paper
[2022-06-05] Printed Article

Cite This Article

陈航, 梁春泉, 王紫, 等. 基于协作推断的正例未标注图学习算法 [J]. 计算机应用研究, 2022, 39 (6): 1694-1699,1748. (Chen Hang, Liang Chunquan, Wang Zi, et al. Positive unlabeled graph learning based on collective inference [J]. Application Research of Computers, 2022, 39 (6): 1694-1699,1748. )

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  • 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.

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