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Edge-Parallel Graph Encoder Embedding (2402.04403v1)
Published 6 Feb 2024 in cs.DC and cs.LG
Abstract: New algorithms for embedding graphs have reduced the asymptotic complexity of finding low-dimensional representations. One-Hot Graph Encoder Embedding (GEE) uses a single, linear pass over edges and produces an embedding that converges asymptotically to the spectral embedding. The scaling and performance benefits of this approach have been limited by a serial implementation in an interpreted language. We refactor GEE into a parallel program in the Ligra graph engine that maps functions over the edges of the graph and uses lock-free atomic instrutions to prevent data races. On a graph with 1.8B edges, this results in a 500 times speedup over the original implementation and a 17 times speedup over a just-in-time compiled version.
- “Incorporating network embedding into markov random field for better community detection,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 160–167, 07 2019.
- “A survey of community detection approaches: From statistical modeling to deep learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 2, pp. 1149–1170, 2023.
- “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, 2016, pp. 855–864.
- “Representation learning on graphs: Methods and applications,” IEEE Data Eng. Bull., vol. 40, pp. 52–74, 2017.
- “On a two-truths phenomenon in spectral graph clustering,” Proceedings of the National Academy of Sciences, vol. 116, no. 13, pp. 5995–6000, 2019.
- “Towards next-generation cybersecurity with graph AI,” SIGOPS Oper. Syst. Rev., vol. 55, no. 1, pp. 61–67, jun 2021.
- “Community detection and classification in hierarchical stochastic blockmodels,” IEEE Transactions on Network Science and Engineering, vol. 4, no. 1, pp. 13–26, 2017.
- “A consistent adjacency spectral embedding for stochastic blockmodel graphs,” Journal of the American Statistical Association, vol. 107, no. 499, pp. 1119–1128, 2012.
- “Statistical inference on random dot product graphs: a survey,” Journal of Machine Learning Research, vol. 18, no. 226, pp. 1–92, 2018.
- “Compressive spectral embedding: sidestepping the SVD,” in Advances in Neural Information Processing Systems, 2015, vol. 28.
- “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, p. 701–710.
- “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907, 2016.
- “One-hot graph encoder embedding,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 6, pp. 7933–7938, 2023.
- “Ligra: A lightweight graph processing framework for shared memory,” SIGPLAN Notices, vol. 48, no. 8, pp. 135–146, 2013.
- “From louvain to leiden: guaranteeing well-connected communities,” Scientific reports, vol. 9, no. 1, pp. 5233, 2019.
- “SNAP Datasets: Stanford large network dataset collection,” http://snap.stanford.edu/data, June 2014.
- “The network data repository with interactive graph analytics and visualization,” in AAAI, 2015.
- “CPMA: An efficient batch-parallel compressed set without pointers,” arXiv preprint arXiv:2305.05055, 2023.