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New node anomaly detection algorithm based on nonnegative matrix factorization for directed citation networks

Author

Listed:
  • Ali Tosyali

    (University of Delaware)

  • Jinho Kim

    (Rutgers University)

  • Jeongsub Choi

    (Rutgers University)

  • Yunyi Kang

    (Arizona State University)

  • Myong K. Jeong

    (Rutgers University)

Abstract

Outlier detection is a crucial task for network data analysis, which identifies abnormal entities that deviate from the rest of the dataset. Ranking in outlierness is often used for identifying abnormal nodes in directed citation networks containing citation relationship among nodes. A challenging issue in outlier ranking is how to leverage the rich graph data of complex citation networks. In this paper, we propose a cluster-based outlier score function to identify outliers in citation networks based on nonnegative matrix factorization (NMF). We first represent the citation data as a directed graph, and cluster the directed graph into logical groupings of nodes using NMF. Based on the clustering results, we obtain the outlier score and ranking for each node using the proposed outlier scoring function. The proposed method leverages the direct and indirect citation links between nodes to measure the graph-based outlierness. We validate the proposed outlier ranking method using small artificial dataset and the real-world U.S. patent data.

Suggested Citation

  • Ali Tosyali & Jinho Kim & Jeongsub Choi & Yunyi Kang & Myong K. Jeong, 2020. "New node anomaly detection algorithm based on nonnegative matrix factorization for directed citation networks," Annals of Operations Research, Springer, vol. 288(1), pages 457-474, May.
    • Handle: RePEc:spr:annopr:v:288:y:2020:i:1:d:10.1007_s10479-019-03508-4
    DOI: 10.1007/s10479-019-03508-4
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    References listed on IDEAS

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    1. Jacques Michel & Bernd Bettels, 2001. "Patent citation analysis.A closer look at the basic input data from patent search reports," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(1), pages 185-201, April.
    2. Lian Duan & Lida Xu & Ying Liu & Jun Lee, 2009. "Cluster-based outlier detection," Annals of Operations Research, Springer, vol. 168(1), pages 151-168, April.
    3. Rajiv D. Banker & Hsihui Chang & Zhiqiang Zheng, 2017. "On the use of super-efficiency procedures for ranking efficient units and identifying outliers," Annals of Operations Research, Springer, vol. 250(1), pages 21-35, March.
    4. Sepideh Kaffash & Marianna Marra, 2017. "Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds," Annals of Operations Research, Springer, vol. 253(1), pages 307-344, June.
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    Cited by:

    1. Ali Tosyali & Behnam Tavakkol, 2024. "A node-based index for clustering validation of graph data," Annals of Operations Research, Springer, vol. 341(1), pages 197-221, October.
    2. Jaeseung Baek & Myong K. Jeong & Elsayed A. Elsayed, 2024. "Spatial randomness-based anomaly detection approach for monitoring local variations in multimode surface topography," Annals of Operations Research, Springer, vol. 341(1), pages 173-195, October.
    3. Yi Yu & Jaeseung Baek & Ali Tosyali & Myong K. Jeong, 2024. "Robust asymmetric non-negative matrix factorization for clustering nodes in directed networks," Annals of Operations Research, Springer, vol. 341(1), pages 245-265, October.
    4. Ja’far Dehghanpour & Nezam Mahdavi-Amiri, 2024. "Orthogonal nonnegative matrix factorization problems for clustering: A new formulation and a competitive algorithm," Annals of Operations Research, Springer, vol. 339(3), pages 1481-1497, August.

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