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GEMvis: a visual analysis method for the comparison and refinement of graph embedding models

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Abstract

Graph embedding, which constructs vector representation of nodes in a network, has shown effectiveness in many graph analysis tasks, such as node classification, node clustering, and link prediction. However, due to the complexity of graph embedding models (GEMs) and their nontransparency of hyperparameters, evaluation and comparison of embedding results in retaining the original graph features, and consequently, the selection of suitable GEMs according to graph analysis tasks are challenging for people. In this paper, we present a visual analysis method, GEMvis, to support the evaluation and comparison of GEMs from the original graph, node metric, and embedding result spaces. The method also supports the online refining of GEM by tuning the parameters in its three components (graph sampling method, neural network structure, and loss function). A series of metrics, R_node metrics, for measuring GEMs’ ability to preserve specific node metrics, such as R_degree and R_closeness, is also proposed to support quantitative evaluation and comparison of GEMs’ ability to preserve original graph features. Finally, three case studies and expert feedback illustrate the effectiveness of GEMvis.

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References

  1. Chen, W., Guo, F., Han, D., Pan, J., Nie, X., Xia, J., Zhang, X.: Structure-based suggestive exploration: a new approach for effective exploration of large networks. IEEE Trans. Vis. Comput. Graph. 25, 555–565 (2018)

    Article  Google Scholar 

  2. Cai, H., Zheng, V.W., Chang, C.C.: A comprehensive survey of graph embedding: problems, techniques and applications. IEEE Trans. Knowl. Data Eng. 30, 1616–1637 (2017)

    Article  Google Scholar 

  3. Liu, Z., Sun, M., Lin, Y., Xie, R.: Knowledge representation learning: a review. J. Comput. Res. Dev. 53, 247–261 (2016)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  5. Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey. Knowl. Based Syst. 151, 78–94 (2018)

    Article  Google Scholar 

  6. Li, Q., Njotoprawiro, K.S., Haleem, H., Chen,Q., Yi, C., Ma, X., EmbeddingVis.: A visual analytics approach to comparative network embedding inspection. In: Proceedings of IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 48–59 (2018)

  7. Chen, Y., Lv, C., Li, Y., Chen, W., Ma, K.L.: Ordered matrix representation supporting the visual analysis of associated data. Sci. China Inf. Sci. 63, 1–3 (2020)

    Article  Google Scholar 

  8. Liu, S., Bremer, P..T., Thiagarajan, J..J., Srikumar, V., Wang, B., Livnat, Y., Pascucci, V.: Visual exploration of semantic relationships in neural word embeddings. IEEE Trans. Vis. Comput. Graph. 24, 553–562 (2017)

    Article  Google Scholar 

  9. Chen, Y., Guan, Z., Zhang, R., Du, X., Wang, Y.: A survey on visualization approaches for exploring association relationships in graph data. J. Vis. 22, 625–639 (2019)

    Article  Google Scholar 

  10. Wang, D., Cui, P., Zhu, W.: Structural deep network embedding, In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234 (2016)

  11. Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)

  12. Ayala, D., Borrego, A., Hernández, I., Rivero, C.R., Ruiz, D.: AYNEC: all you need for evaluating completion techniques in knowledge graphs. In: European Semantic Web Conference, pp. 397–411 (2019)

  13. Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710(2014)

  14. Martin, C., Riebeling, M.: A Process for the Evaluation of Node Embedding Methods in the Context of Node Classification, arXiv preprint arXiv:2005.14683 (2020)

  15. Do, P., Phan, T.H.V.: Developing a BERT based triple classification model using knowledge graph embedding for question answering system. Appl. Intell. 2021, 1–16 (2021)

    Google Scholar 

  16. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077 (2015)

  17. Von, L.T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J.J., Fekete, J.D., Fellner, D.W.: Visual analysis of large graphs: state-of-the-art and future research challenges. Comput. Graph. Forum 30, 1719–1749 (2011)

    Article  Google Scholar 

  18. Pienta, R., Abello, J., Kahng, M., Chau, D.H.: Scalable graph exploration and visualization: sensemaking challenges and opportunities. In: 2015 International conference on Big Data and Smart Computing (BIGCOMP) IEEE, pp. 271–278 (2015)

  19. Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 10 (2008)

    MATH  Google Scholar 

  20. Smilkov, D., Thorat, N., Nicholson, C., Reif, E., Viégas, F.B., Wattenberg, M.: Embedding projector: Interactive visualization and interpretation of embeddings, arXiv preprint arXiv:1611.05469 (2016)

  21. Inselberg, A., Dimsdale, B.: Parallel coordinates for visualizing multi-dimensional geometry. In: Computer Graphics, pp. 25–44 (1987)

  22. Misawa, H., Horio, K., Morotomi, N., Fukuda, K., Taniguchi, H.: Extrapolation of group proximity from member relations using embedding and distribution mapping. IEICE Trans. Inf. Syst. 95, 804–811 (2012)

    Article  Google Scholar 

  23. Nishana, S.S., Surendran, S.: Graph embedding and dimensionality reduction—a survey. Int. J. Comput. Sci. Eng. Technol. 4, 29–34 (2013)

    Google Scholar 

  24. Liu, Y., Jun, E., Li, Q., Heer, J.: Latent space cartography: visual analysis of vector space embeddings. Comput. Graph. Forum 38, 67–78 (2019)

    Article  Google Scholar 

  25. Molino, P., Wang, Y., Zhang, J.: Parallax: Visualizing and understanding the semantics of embedding spaces via algebraic formulae, arXiv preprint arXiv:1905.12099 (2019)

  26. Heimerl, F., Kralj, C., Moller, T., Gleicher, M.: embcomp: Visual interactive comparison of vector embeddings. IEEE Trans. Vis. Comput. Graph. (2020)

  27. Ribeiro, L.F.R., Saverese, P.H.P., Figueiredo, D.R.: struc2vec: Learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 385–394 (2017)

  28. Cao, S., Lu, W., Xu, Q.: Deep neural networks for learning graph representations. In: Proceedings of the AAAI Conference on Artificial Intelligence, p. 30 (2016)

  29. Spielman, D.A., Teng, S.H.: Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems. In: Proceedings of the Thirty-Sixth Annual ACM Symposium on Theory of Computing, pp. 81–90 (2004)

  30. Hamilton, W.L.: Graph representation learning. Synth. Lect. Artif. Intell. Mach. Learn. 14, 1–159 (2014)

    Google Scholar 

  31. Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977)

    Article  Google Scholar 

  32. Parberry, I., Johnson, D.S.: The SIGACT theoretical computer science genealogy: Preliminary report, Electronic Publishing and the Information Superhighway: Enabling Technologies, Issues, Applications, pp. 197–205 (1995)

  33. Wang, Y., Chen, X., Ge, T., Bao, C., Sedlmair, M., Fu, C.W., Deussen, O., Chen, B.: Optimizing color assignment for perception of class separability in multiclass scatterplots. IEEE Trans. Vis. Comput. Graph. 25, 820–829 (2018)

    Article  Google Scholar 

  34. Harrower, M., Brewer, C.A.: ColorBrewer.org: an online tool for selecting colour schemes for maps. Cartogr. J. 40, 27–37 (2003)

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61972010, 62132017, 61972122) and National Key R &D program of China (2018YFC1603602).

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Authors and Affiliations

  1. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, China

    Yi Chen, Qinghui Zhang & Zeli Guan

  2. School of Computer Science and Engineering, Central South University, Hunan, China

    Ying Zhao

  3. State Key Lab of CAD & CG, Zhejiang University, Zhejiang, China

    Wei Chen

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  1. Yi Chen
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  2. Qinghui Zhang
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Correspondence to Yi Chen.

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Chen, Y., Zhang, Q., Guan, Z. et al. GEMvis: a visual analysis method for the comparison and refinement of graph embedding models. Vis Comput 38, 3449–3462 (2022). https://doi.org/10.1007/s00371-022-02548-5

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