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Which Group Do You Belong To? Sentiment-Based PageRank to Measure Formal and Informal Influence of Nodes in Networks

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Complex Networks & Their Applications IX (COMPLEX NETWORKS 2020 2020)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 944))

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Abstract

Organizational networks are often hierarchical by nature as individuals take on roles or functions at various job levels. Prior studies have used either text-level (e.g., sentiment, affect) or structural-level features (e.g., PageRank, various centrality metrics) to identify influential nodes in networks. In this study, we use a combination of these two levels of information to develop a novel ranking method that combines sentiment analysis and PageRank to infer node-level influence in a real-world organizational network. We detect sentiment scores for all actor pairs based on the content of their email-based communication, and calculate their influence index using an enhanced PageRank method. Finally, we group individual nodes into distinct clusters according to their influence index. Compared to established network metrics designed or used to infer formal and informal influence and ground truth data on job levels, our metric achieves the highest accuracy for inferring formal influence (60.7%) and second highest for inferring informal influence (69.0%). Our approach shows that combining text-level and structural-level information is effective for identifying the job level of nodes in an organizational network.

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Notes

  1. 1.

    https://www.cs.cmu.edu/~./enron/.

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Author information

Authors and Affiliations

  1. University of Illinois at Urbana-Champaign, Champaign, USA

    Lan Jiang, Ly Dinh, Rezvaneh Rezapour & Jana Diesner

Authors
  1. Lan Jiang
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  2. Ly Dinh
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  3. Rezvaneh Rezapour
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  4. Jana Diesner
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Corresponding author

Correspondence to Lan Jiang .

Editor information

Editors and Affiliations

  1. Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, Madrid, Madrid, Spain

    Rosa M. Benito

  2. IUT Lumière, University of Lyon, Bron Cedex, France

    Chantal Cherifi

  3. LE2I, UFR Sciences et Techniques, Université de Bourgogne, Dijon, France

    Hocine Cherifi

  4. Grupo Interdisciplinar de Sistemas Complejos, Departamento de Matematicas, Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Center for Social and Biomedical Complexity, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

  6. Department of Chemical Engineering, Universitat Rovira i Virgili, Tarragona, Tarragona, Spain

    Marta Sales-Pardo

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Jiang, L., Dinh, L., Rezapour, R., Diesner, J. (2021). Which Group Do You Belong To? Sentiment-Based PageRank to Measure Formal and Informal Influence of Nodes in Networks. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-65351-4_50

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  • DOI: https://doi.org/10.1007/978-3-030-65351-4_50

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