Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding (2401.06727v1)
Abstract: Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding. Most existing neural network approaches learn latent representations by minimizing reconstruction errors. Rare work considers the data distribution and the topological structure of latent codes simultaneously, which often results in inferior embeddings in real-world graph data. This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) method for attributed graph data to improve the stability and quality of learned representations to tackle the crowding problem. The node-to-node geodesic similarity is preserved between the original and latent space under a pre-defined distribution. The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets, which validates our solutions. We promise to release the code after acceptance.
- Matthew B Hastings, “Community detection as an inference problem,” Physical Review E, vol. 74, no. 3, pp. 035102, 2006.
- “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907, 2016.
- “Netal: a new graph-based method for global alignment of protein–protein interaction networks,” Bioinformatics, vol. 29, no. 13, pp. 1654–1662, 2013.
- “Mgae: Marginalized graph autoencoder for graph clustering,” in CIKM, 2017, pp. 889–898.
- “Deep graph clustering via dual correlation reduction,” in AAAI, 2022, vol. 36, pp. 7603–7611.
- “Variational graph auto-encoders,” arXiv preprint arXiv:1611.07308, 2016.
- “Simgrace: A simple framework for graph contrastive learning without data augmentation,” 2023.
- “Progcl: Rethinking hard negative mining in graph contrastive learning,” in International conference on machine learning. PMLR, 2022.
- Mark EJ Newman, “Finding community structure in networks using the eigenvectors of matrices,” Physical review E, vol. 74, no. 3, pp. 036104, 2006.
- “Community detection in attributed graphs: An embedding approach,” in AAAI, 2018.
- “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014, pp. 701–710.
- “Attributed graph clustering via adaptive graph convolution,” arXiv preprint arXiv:1906.01210, 2019.
- “Attributed graph clustering: A deep attentional embedding approach,” arXiv preprint arXiv:1906.06532, 2019.
- “Learning graph embedding with adversarial training methods,” IEEE transactions on cybernetics, vol. 50, no. 6, pp. 2475–2487, 2019.
- “Graph neural networks: A review of methods and applications,” AI Open, vol. 1, pp. 57–81, 2020.
- “Graphmae: Self-supervised masked graph autoencoders,” knowledge discovery and data mining, 2022.
- Laurens Van der Maaten and Geoffrey Hinton, “Visualizing data using t-sne.,” Journal of machine learning research, vol. 9, no. 11, 2008.
- “Mgae: Marginalized graph autoencoder for graph clustering,” conference on information and knowledge management, 2017.
- “Dlme: Deep local-flatness manifold embedding,” 2022.
- Laurens van der Maaten and Geoffrey E. Hinton, “Visualizing data using t-sne,” Journal of Machine Learning Research, 2008.
- “Umap: Uniform manifold approximation and projection for dimension reduction,” arXiv preprint arXiv:1802.03426, 2018.
- “k-means: A revisit,” Neurocomputing, 2018.
- “Deep graph infomax.,” ICLR, vol. 2, no. 3, pp. 4, 2019.
- “Adaptive graph encoder for attributed graph embedding,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 976–985.
- Costas Mavromatis and G. Karypis, “Graph infoclust: Maximizing coarse-grain mutual information in graphs,” in PAKDD, 2021.
- “Scaling attributed network embedding to massive graphs,” very large data bases, 2020.