Abstract
Many problems such as node classification and link prediction in network data can be solved using graph embeddings. However, it is difficult to use graphs to capture non-binary relations such as communities of nodes. These kinds of complex relations are expressed more naturally as hypergraphs. While hypergraphs are a generalization of graphs, state-of-the-art graph embedding techniques are not adequate for solving prediction and classification tasks on large hypergraphs accurately in reasonable time. In this paper, we introduce HyperNetVec, a novel hierarchical framework for scalable unsupervised hypergraph embedding. HyperNetVec exploits shared-memory parallelism and is capable of generating high quality embeddings for real-world hypergraphs with millions of nodes and hyperedges in only a couple of minutes while existing hypergraph systems either fail for such large hypergraphs or may take days to produce the embeddings.
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References
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013). https://doi.org/10.1109/TPAMI.2013.50
Bordes, A., Usunier, N., Garcia-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of the 26th International Conference on Neural Information Processing Systems (2013)
Bui, T.N., Jones, C.: A heuristic for reducing fill-in in sparse matrix factorization. In: SIAM Conference on Parallel Processing for Scientific Computing, March 1993
Chen, H., Perozzi, B., Hu, Y., Skiena, S.: Harp: Hierarchical representation learning for networks (2017)
dataset, D.: citation dataset dblp. https://www.aminer.org/lab-datasets/citation/DBLP-citation-Jan8.tar.bz
Deng, C., Zhao, Z., Wang, Y., Zhang, Z., Feng, Z.: Graphzoom: a multi-level spectral approach for accurate and scalable graph embedding (2020)
Devine, K.D., Boman, E.G., Heaphy, R.T., Bisseling, R.H., Catalyurek, U.V.: Parallel hypergraph partitioning for scientific computing. In: Proceedings of the 20th International Conference on Parallel and Distributed Processing (2006). http://dl.acm.org/citation.cfm?id=1898953.1899056
FAERS: drug dataset. https://www.fda.gov/Drugs/
Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks (2019)
Getoor, L.: Cora dataset. https://linqs.soe.ucsc.edu/data
Giurgiu, M., et al.: CORUM: the comprehensive resource of mammalian protein complexes-2019. Nucl. Acids Res. (2019)
Grover, A.: High performance implementation of node2vec. https://github.com/snap-stanford/snap/tree/master/examples/node2vec
Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks (2016)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs (2018)
Harper, F.M., Konstan, J.A.: The Movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4) (2015). https://doi.org/10.1145/2827872
Karypis, G., Aggarwal, R., Kumar, V., Shekhar, S.: Multilevel hypergraph partitioning: applications in VLSI domain. IEEE Trans. Very Large Scale Integr. Syst. (1999)
Kirkland, S.: Two-mode networks exhibiting data loss. J. Complex Netw. 6(2), 297–316 (2017). https://doi.org/10.1093/comnet/cnx039
Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning (2018)
Liang, J., Gurukar, S., Parthasarathy, S.: Mile: a multi-level framework for scalable graph embedding (2020)
Maleki, S., Agarwal, U., Burtscher, M., Pingali, K.: Bipart: a parallel and deterministic hypergraph partitioner. SIGPLAN Not. (2021). https://doi.org/10.1145/3437801.3441611
Mateev, N., Pingali, K., Stodghill, P., Kotlyar, V.: Next-generation generic programming and its application to sparse matrix computations. In: Proceedings of the 14th International Conference on Supercomputing. ICS 2000, pp. 88–99. Association for Computing Machinery, New York (2000)
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. KDD 2014. ACM, New York (2014)
Pingali, K., et al.: The tao of parallelism in algorithms. In: PLDI 2011, pp. 12–25 (2011)
Piñero, J., et al.: The DisGeNET knowledge platform for disease genomics: 2019 update. Nucl. Acids Res. 48(D1), D845–D855 (2019)
Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., Tang, J.: Network embedding as matrix factorization. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (2018). https://doi.org/10.1145/3159652.3159706
Rogers, A., Pingali, K.: Compiling for distributed memory architectures. IEEE Trans. Parallel Distrib. Syst. 5(3), 281–298 (1994)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line. In: Proceedings of the 24th International Conference on World Wide Web (2015)
Taubin, G.: A signal processing approach to fair surface design. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques (1995). https://doi.org/10.1145/218380.218473
Tu, K., Cui, P., Wang, X., Wang, F., Zhu, W.: Structural deep embedding for hyper-networks (2018)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? (2019)
Yadati, N., Nimishakavi, M., Yadav, P., Nitin, V., Louis, A., Talukdar, P.: HyperGCN: a new method of training graph convolutional networks on hypergraphs (2019)
Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth (2012)
Zhang, M., Cui, Z., Jiang, S., Chen, Y.: Beyond link prediction: predicting hyperlinks in adjacency space. In: AAAI, pp. 4430–4437 (2018)
Zhang, R., Zou, Y., Ma, J.: Hyper-SAGNN: a self-attention based graph neural network for hypergraphs. In: International Conference on Learning Representations (ICLR) (2020)
Zheng, V.W., Cao, B., Zheng, Y., Xie, X., Yang, Q.: Collaborative filtering meets mobile recommendation: a user-centered approach. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (2010)
Zien, J.Y., Schlag, M.D.F., Chan, P.K.: Multilevel spectral hypergraph partitioning with arbitrary vertex sizes. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. (1999). https://doi.org/10.1109/43.784130
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Maleki, S., Saless, D., Wall, D.P., Pingali, K. (2022). HyperNetVec: Fast and Scalable Hierarchical Embedding for Hypergraphs. In: Ribeiro, P., Silva, F., Mendes, J.F., Laureano, R. (eds) Network Science. NetSci-X 2022. Lecture Notes in Computer Science(), vol 13197. Springer, Cham. https://doi.org/10.1007/978-3-030-97240-0_13
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DOI: https://doi.org/10.1007/978-3-030-97240-0_13
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