Abstract
Network representation learning (NRL) is an important technique for network analysis. Heterogeneous information networks (HINs) contain multiple types of nodes and edges, which describe the personalized information of nodes and complex relationships between nodes. In this paper, we propose a Heterogeneous Adaptive Multi-Channel Graph Convolutional Networks (HAM-GCN) for HIN representation learning. In HAM-GCN, we design three channels to extract the specific and common embeddings with respect to each meta-path from node features, topological structures, and their combinations simultaneously. In addition, we design both channel-level attention and semantic-level attention to fuse the low-dimensional representations obtained from different channels and different meta-paths for learning the final representation. A large number of experiments on three benchmark data sets show that HAM-GCN extracts the most relevant information from topological structure and node features, which significantly improves the classification accuracy than other baseline algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cui, P., Wang, X., Pei, J., et al.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31(5), 833–852 (2019)
Zhang, D., Yin, J., Zhu, X., et al.: Network representation learning: a survey. IEEE Trans. Big Data 6(1), 3–28 (2020)
Wu, J., He, J., Xu, J.: DEMO-Net: degree-specific graph neural networks for node and graph classification. In: KDD, pp 406–415 (2019)
Tu, W., Zhou, S., Liu, X., et al.: Deep fusion clustering network. In: AAAI, pp. 9978–9987 (2021)
Hou, S., Fan, Y., Ju, M., et al.: Disentangled representation learning in heterogeneous information network for large-scale android malware detection in the COVID-19 era and beyond. In: AAAI, pp 7754–7761 (2021)
Scarselli, F., Gori, M., Tsoi, A.C., et al.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61–80 (2009)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR, pp. 1–14 (2017)
Zhuang, C, Ma, Q.: Dual graph convolutional networks for graph-based semi-supervised classification. In: WWW, pp. 499–508 (2018)
Dong, K., Huang, T., Zhou, L., Wang, L., Chen, H.: Deep attributed network embedding based on the PPMI. In: Jensen, C.S., et al. (eds.) DASFAA 2021. LNCS, vol. 12680, pp. 251–266. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73216-5_18
Wang, X., Zhu, M., Bo, D., et al.: AM-GCN: adaptive multi-channel graph convolutional networks. In: KDD, pp 1243–1253 (2020)
Wang, L., Gao, C., Huang, C., et al.: Embedding heterogeneous networks into hyperbolic space without meta-path. In: AAAI, pp 10147–10155 (2021)
Bruna, J., Zaremba, W., Szlam, A., et al.: Spectral networks and locally connected networks on graphs. In: ICLR (2014)
Defferrard, M., Bresson X, Vandergheynst P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: NIPS, pp 3837–3845 (2016)
Monti, F., Boscaini, D., Masci, J., et al.: Geometric deep learning on graphs and manifolds using mixture model CNNs. In: CVPR, pp 5425–5434 (2017)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: KDD, pp 701–710 (2014)
Onrust, L., Bosch, A.V.D., Hamme, H.V.: Improving cross-domain n-gram language modelling with skipgrams. In: ACL, vol. 2 (2016)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD, pp 855–864 (2016)
Tang, J., Qu, M., Wang, M., et al.: Large-scale information network embedding. In: WWW, pp 1067–1077 (2015)
Yang, C., Liu, Z., Zhao, D., et al.: Network representation learning with rich text information. In: IJCAI, pp 2111–2117 (2015)
Fu, T.-Y., Lee, W.-C., Lei, Z.: HIN2Vec: explore meta-paths in heterogeneous information networks for representation learning. In: CIKM, pp 1797–1806 (2017)
Wang, X., Ji, H., Shi, C., et al.: Heterogeneous graph attention network. In: WWW, pp 2022–2032 (2019)
Ren, Y., Liu, B., Huang, C., et al.: Heterogeneous deep graph infomax. CoRR, abs/1911.08538 (2019)
Hu, B., Fang, Y., Shi, C.: Adversarial learning on heterogeneous information networks. In: KDD, pp 120–129 (2019)
Greenfeld, D., Shalit, U.: Robust learning with the hilbert-schmidt independence criterion. In: ICML, pp 3759–3768 (2020)
Du, C., Tu, Z., Jiang. J.: Order-agnostic cross entropy for non-autoregressive machine translation. In: ICML, pp 2849–2859 (2021)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Zhang, Z., Yang, H., Bu, J., et al.: ANRL: attributed network representation learning via deep neural networks. In: IJCAI, pp. 3155–3161 (2018)
Park, C., Han, J., Yu, H.: Deep multiplex graph infomax: attentive multiplex network embedding using global information. Knowl. Based Syst. 197, 105861 (2020)
Cortes, C., Jackel, L.D., Solla, S.A., et al.: Learning curves: asymptotic values and rate of convergence. In: NIPS, pp. 327–334 (1993)
Laurens, V.D.M., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(2605), 2579–2605 (2008)
Wang, L., Fang, Y., Zhou, L.: Preference-based spatial co-location pattern mining. In: Big Data Management. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-7566-9(2022)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (62062066, 61762090, 61966036), Yunnan Fundamental Research Projects (202201AS070015), University Key Laboratory of Internet of Things Technology and Application of Yunnan Province, and the Postgraduate Research and Innovation Foundation of Yunnan University (2021Y024).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Du, J., Zhou, L., Du, G., Wang, L., Jiang, Y. (2022). Heterogeneous Network Representation Learning Based on Adaptive Multi-channel Graph Convolution. In: Wu, H., et al. Spatial Data and Intelligence. SpatialDI 2022. Lecture Notes in Computer Science, vol 13614. Springer, Cham. https://doi.org/10.1007/978-3-031-24521-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-24521-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-24520-6
Online ISBN: 978-3-031-24521-3
eBook Packages: Computer ScienceComputer Science (R0)