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Erschienen in:
CodedVision: Towards Joint Image Understanding and Compression via End-to-End Learning Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Intra-view and Inter-view Attention for Multi-view Network Embedding

verfasst von : Yueyang Wang, Liang Hu, Yueting Zhuang, Fei Wu

Erschienen in: Advances in Multimedia Information Processing – PCM 2018

Verlag: Springer International Publishing

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Abstract

Network Embedding, which represents nodes in networks with efficient low-dimensional vectors, has been proved useful in a variety of applications. However, most existing approaches study single-view networks but not the multi-view networks with multiple types of relationships between nodes. Meanwhile, they ignore the rich features associated with the nodes, which is common in real world. In this paper, we propose a novel network embedding method, Intra-view and Inter-view attention for Multi-view Network Embedding (I2MNE), which leverages both the multi-view network structure and the node features to efficiently generate node representations. Specially, we introduce the intra-view attention when aggregating node features from neighbors for each single view and the inter-view attention when integrating representations across different views. Experiments on two real-world networks show that our approach outperforms other counterpart network embedding methods.

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Vorheriges Kapitel Conditional Feature Coupling Network for Multi-persons Clothing Parsing
Nächstes Kapitel Multi-modal Sequence to Sequence Learning with Content Attention for Hotspot Traffic Speed Prediction
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1. IEEE Trans. Parallel Distrib. Syst. 2. STOC 3. IEEE Communications Magazine 4. ACM Trans. Graph. 5. CHI 6. ACL 7. CVPR 8. WWW.
 
Literatur
1.
Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: ACM International Conference on Web Search and Data Mining, pp. 635–644 (2011)
2.
Bhagat, S., Cormode, G., Muthukrishnan, S.: Node classification in social networks. Comput. Sci. 16(3), 115–148 (2012)
3.
Ding, C.H.Q., He, X., Zha, H., Gu, M., Simon, H.D.: A min-max cut algorithm for graph partitioning and data clustering. In: IEEE International Conference on Data Mining, pp. 107–114 (2001)
4.
Elkahky, A.M., Song, Y., He, X.: A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: International Conference on World Wide Web, pp. 278–288 (2015)
5.
Greene, D.: A matrix factorization approach for integrating multiple data views. In: European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 423–438 (2009)CrossRef
6.
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 855 (2016)
7.
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024–1034 (2017)
8.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks (2016)
9.
Lau, J.H., Baldwin, T.: An empirical evaluation of doc2vec with practical insights into document embedding generation (2016)
10.
Liu, J., Wang, C., Gao, J., Han, J.: Multi-view clustering via joint nonnegative matrix factorization (2013)
11.
Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1412–1421 (2015)
12.
Ma, G., et al.: Multi-view clustering with graph embedding for connectome analysis. In: ACM on Conference on Information and Knowledge Management, pp. 127–136 (2017)
13.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: International Conference on Neural Information Processing Systems, pp. 3111–3119 (2013)
14.
Mnih, A., Teh, Y.W.: A fast and simple algorithm for training neural probabilistic language models. In: International Conference on International Conference on Machine Learning, pp. 419–426 (2012)
15.
Perozzi, B., Alrfou, R., Skiena, S.: DeepWalk: online learning of social representations, pp. 701–710 (2014)
16.
Qu, M., Tang, J., Shang, J., Ren, X., Zhang, M., Han, J.: An attention-based collaboration framework for multi-view network representation learning, pp. 1767–1776 (2017)
17.
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 399–421 (1986)CrossRef
18.
Shi, C., Zhang, Z., Luo, P., Yue, Y., Yue, Y., Wu, B.: Semantic path based personalized recommendation on weighted heterogeneous information networks. In: ACM International on Conference on Information and Knowledge Management, pp. 453–462 (2015)
19.
Shi, Y., Han, F., He, X., Yang, C., Luo, J., Han, J.: mvn2vec: preservation and collaboration in multi-view network embedding (2018)
20.
Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 650–658 (2008)
21.
Sun, Y., Barber, R., Gupta, M., Aggarwal, C.C., Han, J.: Co-author relationship prediction in heterogeneous bibliographic networks. In: International Conference on Advances in Social Networks Analysis and Mining, pp. 121–128 (2011)
22.
Sun, Y., Han, J., Aggarwal, C.C., Chawla, N.V.: When will it happen?: relationship prediction in heterogeneous information networks, pp. 663–672 (2012)
23.
Swami, A., Swami, A., Swami, A.: metapath2vec: scalable representation learning for heterogeneous networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135–144 (2017)
24.
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: International Conference on World Wide Web (2015)
25.
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998 (2008)
26.
Wang, X., Tang, L., Liu, H., Wang, L.: Learning with multi-resolution overlapping communities. Knowl. Inf. Syst. 36(2), 517–535 (2013)CrossRef
27.
Xia, T., Tao, D., Mei, T., Zhang, Y.: Multiview spectral embedding. IEEE Trans. Syst. Man Cybern. Part B 40(6), 1438–1446 (2010)CrossRef
28.
Zhou, D., Burges, C.J.C.: Spectral clustering and transductive learning with multiple views. In: Proceedings of the Twenty-Fourth International Conference on Machine Learning, pp. 1159–1166 (2007)
Metadaten
Titel
Intra-view and Inter-view Attention for Multi-view Network Embedding
verfasst von
Yueyang Wang
Liang Hu
Yueting Zhuang
Fei Wu
Copyright-Jahr
2018
Buch
Advances in Multimedia Information Processing – PCM 2018

Print ISBN: 978-3-030-00775-1

Electronic ISBN: 978-3-030-00776-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-00776-8_19