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Generalizing the Hypergraph Laplacian via a Diffusion Process with Mediators

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Book cover Computing and Combinatorics (COCOON 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10976))

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Abstract

In a recent breakthrough STOC 2015 paper, a continuous diffusion process was considered on hypergraphs (which has been refined in a recent JACM 2018 paper) to define a Laplacian operator, whose spectral properties satisfy the celebrated Cheeger’s inequality. However, one peculiar aspect of this diffusion process is that each hyperedge directs flow only from vertices with the maximum density to those with the minimum density, while ignoring vertices having strict in-beween densities.

In this work, we consider a generalized diffusion process, in which vertices in a hyperedge can act as mediators to receive flow from vertices with maximum density and deliver flow to those with minimum density. We show that the resulting Laplacian operator still has a second eigenvalue satisfying the Cheeger’s inequality.

Our generalized diffusion model shows that there is a family of operators whose spectral properties are related to hypergraph conductance, and provides a powerful tool to enhance the development of spectral hypergraph theory. Moreover, since every vertex can participate in the new diffusion model at every instant, this can potentially have wider practical applications.

The full version of this paper is available online [3].

T.-H. H. Chan—This work was partially supported by the Hong Kong RGC under the grant 17200817.

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Notes

  1. 1.

    In fact, as shown in the full version [3], a stronger upper bound holds: \(\phi _H \le \sqrt{2 \gamma _2}\).

  2. 2.

    In the literature, the weighted Laplacian is actually \(\mathsf {W} \mathsf {L} _w\) in our notation. Hence, to avoid confusion, we restrict the term Laplacian to the normalized space.

References

  1. Alon, N.: Eigenvalues and expanders. Combinatorica 6(2), 83–96 (1986)

    Article  MathSciNet  Google Scholar 

  2. Alon, N., Milman, V.D.: \(\lambda \)1, isoperimetric inequalities for graphs, and superconcentrators. J. Comb. Theory Ser. B 38(1), 73–88 (1985)

    Article  MathSciNet  Google Scholar 

  3. Chan, T.-H.H., Liang, Z.: Generalizing the hypergraph Laplacian via a diffusion process with mediators. arXiv e-prints (2018)

    Google Scholar 

  4. Chan, T.-H.H., Louis, A., Tang, Z.G., Zhang, C.: Spectral properties of hypergraph Laplacian and approximation algorithms. J. ACM 65(3), 15:1–15:48 (2018)

    Article  MathSciNet  Google Scholar 

  5. Chan, T.-H.H., Tang, Z.G., Wu, X., Zhang, C.: Diffusion operator and spectral analysis for directed hypergraph Laplacian. CoRR, abs/1711.01560 (2017)

    Google Scholar 

  6. Chung, F.R.K.: Spectral Graph Theory, vol. 92. American Mathematical Society (1997)

    Google Scholar 

  7. Danisch, M., Chan, T.-H.H., Sozio, M.: Large scale density-friendly graph decomposition via convex programming. In: WWW, pp. 233–242. ACM (2017)

    Google Scholar 

  8. Hein, M., Setzer, S., Jost, L., Rangapuram, S.S.: The total variation on hypergraphs - learning on hypergraphs revisited. In: NIPS, pp. 2427–2435 (2013)

    Google Scholar 

  9. Hoory, S., Linial, N., Wigderson, A.: Expander graphs and their applications. Bull. Am. Math. Soc. 43(4), 439–561 (2006)

    Article  MathSciNet  Google Scholar 

  10. Kannan, R., Vempala, S., Vetta, A.: On clusterings: good, bad and spectral. J. ACM 51(3), 497–515 (2004)

    Article  MathSciNet  Google Scholar 

  11. Louis, A.: Hypergraph Markov operators, eigenvalues and approximation algorithms. In: STOC, pp. 713–722. ACM (2015)

    Google Scholar 

  12. Makarychev, K., Makarychev, Y., Vijayaraghavan, A.: Correlation clustering with noisy partial information. In: COLT. JMLR Workshop and Conference Proceedings, vol. 40, pp. 1321–1342. JMLR.org (2015)

    Google Scholar 

  13. Peng, R., Sun, H., Zanetti, L.: Partitioning well-clustered graphs: spectral clustering works! In: COLT. JMLR Workshop and Conference Proceedings, vol. 40, pp. 1423–1455. JMLR.org (2015)

    Google Scholar 

  14. Yoshida, Y.: Nonlinear Laplacian for digraphs and its applications to network analysis. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 483–492. ACM (2016)

    Google Scholar 

  15. Zhang, C., Hu, S., Tang, Z.G., Chan, T.-H.H.: Re-revisiting learning on hypergraphs: confidence interval and subgradient method. In: ICML. Proceedings of Machine Learning Research, vol. 70, pp. 4026–4034. PMLR (2017)

    Google Scholar 

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Correspondence to T.-H. Hubert Chan .

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Chan, TH.H., Liang, Z. (2018). Generalizing the Hypergraph Laplacian via a Diffusion Process with Mediators. In: Wang, L., Zhu, D. (eds) Computing and Combinatorics. COCOON 2018. Lecture Notes in Computer Science(), vol 10976. Springer, Cham. https://doi.org/10.1007/978-3-319-94776-1_37

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  • DOI: https://doi.org/10.1007/978-3-319-94776-1_37

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