Abstract
Entities in networks may interact positively as well as negatively with each other, which may be modeled by a signed network containing both positive and negative edges between nodes. Understanding how entities behave and not just with whom they interact positively or negatively leads us to the new problem of structural role mining in signed networks. We solve this problem by developing structural node embedding methods that build on sociological theory and technical advances developed specifically for signed networks. With our methods, we can not only perform node-level role analysis, but also solve another new problem of characterizing entire signed networks to make network-level predictions. We motivate our work with an application to social media analysis, where we show that our methods are more insightful and effective at detecting user-level and session-level malicious online behavior from the network structure than previous approaches based on feature engineering.
Work done while author was an intern at the Information Sciences Institute.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chiang, K.-Y., Hsieh, C.-J., Natarajan, N., Dhillon, I.S., Tewari, A.: Prediction and clustering in signed networks: a local to global perspective. JMLR 15(1), 1177–1213 (2014)
Derr, T., Ma, Y., Tang, J.: Signed graph convolutional networks. In: ICDM (2018)
Derr, T., Wang, C., Wang, S., Tang, J.: Signed node relevance measurements. arXiv preprint: arXiv:1710.07236 (2017)
Donnat, C., Zitnik, M., Hallac, D., Leskovec, J.: Learning structural node embedding’s via diffusion wavelets. In: KDD (2018)
Errica, F., Podda, M., Bacciu, D., Micheli, A.: A fair comparison of graph neural networks for graph classification. In: ICLR (2020)
Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey. Knowl. Based Syst. 151, 78–94 (2018)
Heider, F.: The Psychology of Interpersonal Relations. Psychology Press, Hove (2013)
Heimann, M., Safavi, T., Koutra, D.: Distribution of node embedding’s as multi-resolution features for graphs. In: ICDM (2019)
Heimann, M., Shen, H., Safavi, T., Danai, K.: Representation learning-based graph alignment. In: CIKM, Regal (2018)
Hosseinmardi, H., Mattson, S.A., Rafiq, R.I., Han, R., Qin, L., Mishra, S.: Analyzing labeled cyberbullying incidents on the instagram social network. In: SocInfo (2015)
Hosseinmardi, H., Rafiq, R.I., Han, R., Qin, L., Mishra, S.: Prediction of cyberbullying incidents in a media-based social network. In: ASONAM (2016)
Hutto, C.J., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: ICWSM (2014)
Jin, D., Heimann, M., Safavi, T., Wang, M., Lee, W., Snider, L., Danai, K.: Inferring professional roles in email networks. In: KDD, Smart roles (2019)
Jin, J., Heimann, M., Jin, D., Koutra, D.: Understanding and evaluating structural node embedding’s. In: KDD MLG Workshop (2020)
Kao, H.-T., Yan, S., Huang, D., Bartley, Homa, N., Mardi, H., Ferrara, E.: Understanding cyberbullying on Instagram and Ask. fm via social role detection. In: WWW Companion (2019)
Kipf , T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
Kunegis, J., Lommatzsch, A., Bauckhage, C.: The slashdot zoo: mining a social network with negative edges. In: WWW (2009)
Ribeiro, L.F.R., Saverese, P.H.P., Figueiredo, D.R.: Struc2vec: learning node representations from structural identity. In: KDD (2017)
Rossi, R.A., Ahmed, N.K.: Role discovery in networks. TKDE 27(4), 1112–1131 (2014)
Rossi, R.A., Jin, D., Kim, S., Ahmed, N.K., Koutra, D., Lee, J.B.: On proximity and structural role-based embedding’s in networks: misconceptions, methods, and applications. TKDD 14, 1–13 (2020)
Shervashidze, N., Vishwanathan, S.V.N., Petri, T., Mehlhorn, K., Borgwardt, K.: Efficient graphlet kernels for large graph comparison. In: AISTATS (2009)
Wang, S., Tang, J., Aggarwal, C., Chang, Y., Liu, H.: Signed network embedding in social media. In: SDM (2017)
Yuan, S., Wu, X., Xiang, Y.: SNE: signed network embedding. In: PAKDD (2017)
Acknowledgments
The authors are grateful to the Defense Advanced Research Projects Agency (DARPA), contract W911NF-17-C-0094, for their support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Heimann, M., Murić, G., Ferrara, E. (2021). Structural Node Embedding in Signed Social Networks: Finding Online Misbehavior at Multiple Scales. 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_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-65351-4_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-65350-7
Online ISBN: 978-3-030-65351-4
eBook Packages: EngineeringEngineering (R0)