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Validating Vector-Label Propagation for Graph Embedding

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Cooperative Information Systems (CoopIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13591))

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Abstract

Structural network analysis retrieves the holistic patterns of interactions among network instances. Due to the unprecedented growth of data availability, it is time to take advantage of Machine Learning to integrate the outcome of the structural analysis with better predictions on the upcoming states of large networks. Concerning the existing challenges of adopting methods embracing multi-dimensional, multi-task, transparent representations within incremental procedures, in our recent study, we proposed the AVPRA algorithm. It works as an embedder of both the network structure and domain-specific features making the aforementioned challenges feasible to address. In this paper, we elaborate on the validation of AVPRA by adopting it in multiple downstream Machine Learning tasks on the Twitter network of the Italian Parliament. Comparing the outcome with state-of-the-art algorithms of graph embedding, the capability of AVPRA in retaining either network structure properties or domain-specific features of the nodes is promising. In addition, the method is incremental and transparent.

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Notes

  1. 1.

    In particular we used the friendship relation accessible from Twitter API.

  2. 2.

    https://networkx.org/.

  3. 3.

    https://www.mapequation.org/infomap/.

References

  1. Abbas, A.M.: Social network analysis using deep learning: applications and schemes. Soc. Netw. Anal. Min. 11(1), 1–21 (2021)

    Article  Google Scholar 

  2. Azaouzi, M., Romdhane, L.B.: An evidential influence-based label propagation algorithm for distributed community detection in social networks. Proc. Comput. Sci. 112, 407–416 (2017)

    Article  Google Scholar 

  3. Azzini, A., et al.: Advances in data management in the big data era. In: Goedicke, M., Neuhold, E., Rannenberg, K. (eds.) Advancing Research in Information and Communication Technology. IAICT, vol. 600, pp. 99–126. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-81701-5_4

    Chapter  Google Scholar 

  4. Bader, D.A., Kintali, S., Madduri, K., Mihail, M.: Approximating betweenness centrality. In: Bonato, A., Chung, F.R.K. (eds.) WAW 2007. LNCS, vol. 4863, pp. 124–137. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77004-6_10

    Chapter  Google Scholar 

  5. Bellandi, V., Ceravolo, P., Maghool, S., Siccardi, S.: Toward a general framework for multimodal big data analysis. Big Data (2022)

    Google Scholar 

  6. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)

    Google Scholar 

  7. Bonner, S., Brennan, J., Kureshi, I., Theodoropoulos, G., McGough, A.S., Obara, B.: Evaluating the quality of graph embeddings via topological feature reconstruction. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 2691–2700 (2017). https://doi.org/10.1109/BigData.2017.8258232

  8. Borgatti, S.P., Halgin, D.S.: On network theory. Organ. Sci. 22(5), 1168–1181 (2011)

    Article  Google Scholar 

  9. Cao, S., Lu, W., Xu, Q.: Grarep: learning graph representations with global structural information. In: International Conference on Information and Knowledge Management (CIKM), pp. 891–900 (2015)

    Google Scholar 

  10. Chiesi, A.: Network analysis. In: Smelser, N.J., Baltes, P.B. (eds.) International Encyclopedia of the Social & Behavioral Sciences, Pergamon, Oxford, pp. 10499–10502 (2001). https://doi.org/10.1016/B0-08-043076-7/04211-X,https://www.sciencedirect.com/science/article/pii/B008043076704211X

  11. Emirbayer, M., Goodwin, J.: Network analysis, culture, and the problem of agency. Am. J. Sociol. 99(6), 1411–1454 (1994)

    Article  Google Scholar 

  12. Grando, F., Granville, L.Z., Lamb, L.C.: Machine learning in network centrality measures: tutorial and outlook. ACM Comput. Surv. (CSUR) 51(5), 1–32 (2018)

    Article  Google Scholar 

  13. Grando, F., Lamb, L.C.: On approximating networks centrality measures via neural learning algorithms. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 551–557. IEEE (2016)

    Google Scholar 

  14. Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103018 (2010)

    Google Scholar 

  15. Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 855–864 (2016)

    Google Scholar 

  16. Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Conference on Advances in Neural Information Processing Systems (NIPS), pp. 1024–1034 (2017)

    Google Scholar 

  17. Hasan, M.A., Zaki, M.J.: A survey of link prediction in social networks. In: Social network data analytics, pp. 243–275. Springer (2011). https://doi.org/10.1007/978-1-4419-8462-3_9

  18. Jokar, E., Mosleh, M.: Community detection in social networks based on improved label propagation algorithm and balanced link density. Phys. Lett. A 383(8), 718–727 (2019)

    Article  MathSciNet  Google Scholar 

  19. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016)

  20. Li, Q., Zhou, T., Lü, L., Chen, D.: Identifying influential spreaders by weighted leaderrank. Phys. A 404, 47–55 (2014)

    Article  MathSciNet  Google Scholar 

  21. Lim, M., Abdullah, A., Jhanjhi, N., Khan, M.K.: Situation-aware deep reinforcement learning link prediction model for evolving criminal networks. IEEE Access 8, 16550–16559 (2019)

    Article  Google Scholar 

  22. Martínez, V., Berzal, F., Cubero, J.C.: A survey of link prediction in complex networks. ACM Comput. Surv. (CSUR) 49(4), 1–33 (2016)

    Article  Google Scholar 

  23. McDaid, A.F., Greene, D., Hurley, N.: Normalized mutual information to evaluate overlapping community finding algorithms. arXiv preprint arXiv:1110.2515 (2011)

  24. Mendonça, M.R., Barreto, A.M., Ziviani, A.: Approximating network centrality measures using node embedding and machine learning. IEEE Trans. Netw. Sci. Eng. 8(1), 220–230 (2020)

    Article  MathSciNet  Google Scholar 

  25. Nurek, M., Michalski, R.: Combining machine learning and social network analysis to reveal the organizational structures. Appl. Sci. 10(5), 1699 (2020)

    Google Scholar 

  26. Opitz, J., Burst, S.: Macro f1 and macro f1. arXiv preprint arXiv:1911.03347 (2019)

  27. Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1105–1114 (2016)

    Google Scholar 

  28. Palmonari, M., Minervini, P.: Knowledge graph embeddings and explainable ai. Knowl. Graphs Explain. Artifi. Intell. Found. Appli. Challenges 47, 49 (2020)

    Google Scholar 

  29. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 701–710 (2014)

    Google Scholar 

  30. Perozzi, B., Kulkarni, V., Chen, H., Skiena, S.: Don’t walk, skip! online learning of multi-scale network embeddings. In: International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 258–265 (2017)

    Google Scholar 

  31. Rossi, A., Barbosa, D., Firmani, D., Matinata, A., Merialdo, P.: Knowledge graph embedding for link prediction: a comparative analysis. ACM Trans. Knowl. Discovery Data (TKDD) 15(2), 1–49 (2021)

    Article  Google Scholar 

  32. Rosvall, M., Bergstrom, C.T.: Maps of information flow reveal community structure in complex networks. arXiv preprint physics.soc-ph/0707.0609 (2007)

    Google Scholar 

  33. Salehi Rizi, F., Granitzer, M.: Properties of vector embeddings in social networks. Algorithms 10(4), 109 (2017)

    Google Scholar 

  34. Silva, T.C., Zhao, L.: Machine Learning in Complex Networks. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-17290-3

  35. Sun, H., Huang, J., Zhong, X., Liu, K., Zou, J., Song, Q.: Label propagation with-degree neighborhood impact for network community detection. Comput. Intell. Neurosci. 2014, 130689 (2014)

    Google Scholar 

  36. Traag, V.A., Waltman, L., Van Eck, N.J.: From louvain to leiden: guaranteeing well-connected communities. Sci. Rep. 9(1), 1–12 (2019)

    Article  Google Scholar 

  37. Bellandi, V., Ceravolo, P., Damiani, E., Maghool, S.: Agent-based vector- label propagation for explaining social network structures. CCIS, vol. 1593 (2022). https://doi.org/10.1007/978-3-031-07920-7_24

  38. Wang, X., Cui, P., Wang, J., Pei, J., Zhu, W., Yang, S.: Community preserving network embedding. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  39. Xie, J., Szymanski, B.K., Liu, X.: Slpa: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: 2011 IEEE 11th International Conference on Data Mining Workshops, pp. 344–349. IEEE (2011)

    Google Scholar 

  40. Xing, Y., Meng, F., Zhou, Y., Zhu, M., Shi, M., Sun, G.: A node influence based label propagation algorithm for community detection in networks. Sci. World J. 2014, 627581 (2014)

    Google Scholar 

  41. Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.: Scan: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 824–833 (2007)

    Google Scholar 

  42. Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Università degli Studi di Milano (UNIMI), Milan, Italy

    Valerio Bellandi, Ernesto Damiani, Valerio Ghirimoldi & Samira Maghool

  2. Università degli Studi di Milano-Bicocca, Milan, Italy

    Fedra Negri

Authors
  1. Valerio Bellandi
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  2. Ernesto Damiani
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  3. Valerio Ghirimoldi
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  4. Samira Maghool
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  5. Fedra Negri
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Corresponding author

Correspondence to Samira Maghool .

Editor information

Editors and Affiliations

  1. Telecom SudParis - Institut Polytechnique de Paris, Evry, France

    Mohamed Sellami

  2. University of Milan, Milan, Italy

    Paolo Ceravolo

  3. Utrecht University, Utrecht, The Netherlands

    Hajo A. Reijers

  4. Telecom SudParis - Institut Polytechnique de Paris, Evry, France

    Walid Gaaloul

  5. University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

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Bellandi, V., Damiani, E., Ghirimoldi, V., Maghool, S., Negri, F. (2022). Validating Vector-Label Propagation for Graph Embedding. In: Sellami, M., Ceravolo, P., Reijers, H.A., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2022. Lecture Notes in Computer Science, vol 13591. Springer, Cham. https://doi.org/10.1007/978-3-031-17834-4_15

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  • DOI: https://doi.org/10.1007/978-3-031-17834-4_15

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