Abstract
Graph embedding has been extensively studied in the literature and is widely used in various applications such as drug discovery, social network analysis, and natural language processing. However, existing approaches ignore the attribute information or are limited to learning graph representations at certain graph scales without considering the layer-wise community structure to improve embedding learning. To tackle these problems, we propose a Community-aware graph Embedding method with Multi-level attribute integration, a novel attributed graph embedding framework. It first coarsens the topological structure and attributes information alternating in a parallel strategy. Then in each coarsened layer, a self-attention mechanism is adopted to naturally integrate node attributes and obtain node embedding representations. Next, we introduce neighbor propagation at the same layer, cross-layer community propagation, and consider community information of nodes in the coarsening process to modify the embedding representation. Compared with the previous graph embedding methods, experimental results in different application scenarios on real-world datasets demonstrate the effectiveness of our proposed algorithm.
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References
Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22nd ACM international conference on knowledge discovery and data mining (SIGKDD), pp 1225–1234
Ktena SI, Parisot S, Ferrante E, Rajchl M, Lee M, Glocker B, Rueckert D (2017) Distance metric learning using graph convolutional networks: application to functional brain networks. In: Medical image computing and computer assisted intervention MICCAI 2017: 20th international conference, Quebec City, September 11–13, 2017, Proceedings, Part I 20. Springer, pp 469–477
Lin H, Ma J, Cheng M, Yang Z, Chen L, Chen G (2021) Rumor detection on twitter with claim-guided hierarchical graph attention networks. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp 10035–10047
Ma J, Zhou C, Cui P, Yang H, Zhu W (2019) Learning disentangled representations for recommendation. In: Advances in neural information processing systems, vol 32
Bastings J, Titov I, Aziz W, Marcheggiani D, Sima’an K (2017) Graph convolutional encoders for syntax-aware neural machine translation. The Association for Computational Linguistics
Zhang D, Yin J, Zhu X, Zhang C (2018) Network representation learning: a survey. IEEE Trans Big Data 6(1):3–28
Fang D, Zhang J, Ji N, Junying H, Zhang C (2019) Discriminative representation learning with supervised auto-encoder. Neural Process Lett 49(2):507–520
Zihan Zhou YG, Ge Yu (2021) Adversarial network embedding using structural similarity. Front Comput Sci 15(1):151603
Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM international conference on knowledge discovery and data mining (SIGKDD), pp 701–710
Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM international conference on knowledge discovery and data mining (SIGKDD), pp 855–864
Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: Proceedings of the 24th international conference on World Wide Web (WWW), pp 1067–1077
Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems (NIPS), pp 1024–1034
Scarselli F, Marco Gori A, Tsoi C, Hagenbuchner M, Monfardini G (2008) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80
Chen H, Perozzi B, Hu Y, Skiena S (2018) Harp: Hierarchical representation learning for networks. In: Proceedings of the conference on artificial intelligence (AAAI), pp 2127–2134
Liang J, Gurukar S, Parthasarathy S (2021) Mile: a multi-level framework for scalable graph embedding. In: Proceedings of the international AAAI conference on web and social media (ICWSM), pp 361–372
Deng C, Zhao Z, Wang Y, Zhang Z, Feng Z (2020) GraphZoom: a multi-level spectral approach for accurate and scalable graph embedding. In: Proceedings of the international conference on learning representations (ICLR)
Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2018) Graph attention networks. In: Proceedings of the international conference on learning representations (ICLR)
Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. In: Proceedings of the international conference on learning representations (ICLR)
Velickovic P, Fedus W, Hamilton WL, Liò P, Yoshua B, Hjelm RD (2019) Deep graph infomax. In: Proceedings of the international conference on learning representations (ICLR)
Yang C, Liu Z, Zhao D, Sun M, Chang EY (2015) Network representation learning with rich text information. In: Proceedings of the twenty-fourth international joint conference on artificial intelligence (IJCAI), pp 2111–2117
He R, Lee WS, Ng HT, Dahlmeier D (2017) An unsupervised neural attention model for aspect extraction. In: Proceedings of the 55th annual meeting of the association for computational linguistics, pp 388–397
Gao H, Huang H (2018) Deep attributed network embedding. In: The proceedings of the twenty-seventh international joint conference on artificial intelligence (IJCAI), pp 3364–3370
Salehi A, Davulcu H (2020) Graph attention auto-encoders. In: 2020 IEEE 32nd international conference on tools with artificial intelligence (ICTAI), pp 989–996
Fu G, Hou C, Yao X (2019) Learning topological representation for networks via hierarchical sampling. In: 2019 International joint conference on neural networks (IJCNN), pp 1–8
Zhang Z, Yang C, Liu Z, Sun M, Fang Z, Zhang B, Lin L (2022) COSINE: compressive network embedding on large-scale information networks. IEEE Trans Knowl Data Eng 34(8):3655–3668
Shuman DI, Narang SK, Frossard P, Ortega A, Vandergheynst P (2013) The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process Mag 30(3):83–98
Livne OE, Brandt A (2012) Lean algebraic multigrid (LAMG): fast graph Laplacian linear solver. SIAM J Sci Comput 34(4):B499–B522
Zhang J, Dong Y, Wang Y, Tang J, Ding M (2019) ProNE: fast and scalable network representation learning. IJCAI 19:4278–4284
Younis O, Krunz M, Ramasubramanian S (2006) Node clustering in wireless sensor networks: recent developments and deployment challenges. IEEE Netw 20(3):20–25
Ghamrawi N, McCallum A (2005) Collective multi-label classification. In: Proceedings of the 14th ACM international conference on information and knowledge management, pp 195–200
Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145–1159
Shchur O, Mumme M, Bojchevski A, Günnemann S (2018) Pitfalls of graph neural network evaluation. Computing Research Repository. arXiv:1811.05868
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Li, Y., Wang, W., Wei, J. et al. Community-aware graph embedding via multi-level attribute integration. Knowl Inf Syst 65, 5635–5655 (2023). https://doi.org/10.1007/s10115-023-01928-0
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DOI: https://doi.org/10.1007/s10115-023-01928-0