Abstract
The timely rating forecast of customers is crucial for the evaluation of trading service. Due to the high complexity and variability of rating, traditional static graph models have been proved to be ineffective in learning the topological and temporal features of dependent nodes and the representation of edge weights. In this paper, we propose a trading evaluation prediction method on the bitcoin marketplace and a novel deep learning framework to tackle the dependent problem of topological and temporal information. We elaborate a hierarchical framework including dynamic rating construction network layer, extracting node features layer and edge learner layer. Firstly, we obtain the data and organize them into a graph sequence as input. Secondly, we learn the node representation in the extracting node features layer by using the evolving graph neural network to learn the dependency of topological and temporal information. Finally, we use cross projection to learn the edge representation in the edge learner layer. Experimental results show that our model can effectively predict the edge weights and outperforms state-of-art baselines on various real-world transaction datasets, which give a more accurate evaluation in trading evaluation.
Similar content being viewed by others
References
Abu-El-Haija S, Perozzi B, Al-Rfou R (2017) Learning edge representations via low-rank asymmetric projections. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1787–1796
Abu-El-Haija S, Perozzi B, Al-Rfou R, Alemi AA (2018) Watch your step: learning node embeddings via graph attention. In: Advances in neural information processing systems, pp 9180–9190
Chen H, Li Y, Sun X, Xu G, Yin H (2021) Temporal meta-path guided explainable recommendation. In: Proceedings of the 14th ACM international conference on web search and data mining, pp 1056–1064
Chen H, Yin H, Chen T, Nguyen Q V H, Peng W C, Li X (2019) Exploiting centrality information with graph convolutions for network representation learning. In: 2019 IEEE 35th International conference on data engineering (ICDE). IEEE, pp 590–601
Chen H, Yin H, Sun X, Chen T, Gabrys B, Musial K (2020) Multi-level graph convolutional networks for cross-platform anchor link prediction. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1503–1511
Chen T, Yin H, Chen H, Wu L, Wang H, Zhou X, Li X (2018) Tada: trend alignment with dual-attention multi-task recurrent neural networks for sales prediction. In: 2018 IEEE International conference on data mining (ICDM). IEEE, pp. 49–58
Cho K, van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). https://doi.org/10.3115/v1/D14-1179. https://www.aclweb.org/anthology/D14-1179. Association for Computational Linguistics, Doha, pp 1724–1734
Dauphin Y N, Fan A, Auli M, Grangier D (2017) Language modeling with gated convolutional networks. In: Proceedings of the 34th international conference on machine learning - volume 70, ICML’17, p 933–941. JMLR.org
Gao H, Ji S (2019) Graph u-nets. In: ICML
Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 855–864
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79(8):2554–2558
Kipf T N, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations (ICLR)
Kumar S, Hooi B, Makhija D, Kumar M, Faloutsos C, Subrahmanian V (2018) Rev2: Fraudulent user prediction in rating platforms. In: Proceedings of the Eleventh ACM international conference on web search and data mining. ACM, pp 333– 341
Kumar S, Spezzano F, Subrahmanian V, Faloutsos C (2016) Edge weight prediction in weighted signed networks. In: 2016 IEEE 16th International conference on data mining (ICDM). IEEE, pp 221–230
Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp 1188–1196
LeCun Y, Boser B, Denker J S, Henderson D, Howard R E, Hubbard W, Jackel L D (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551
Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791
Li J, Dani H, Hu X, Tang J, Chang Y, Liu H (2017) Attributed network embedding for learning in a dynamic environment. In: Proceedings of the 2017 ACM on conference on information and knowledge management, CIKM ’17. https://doi.org/10.1145/3132847.3132919. Association for Computing Machinery, New York, pp 387–396
Manessi F, Rozza A, Manzo M (2020) Dynamic graph convolutional networks. Pattern Recogn 97:107000
Narayan A, Roe P H (2018) Learning graph dynamics using deep neural networks. IFAC-PapersOnLine 51(2):433–438
Pareja A, Domeniconi G, Chen J, Ma T, Suzumura T, Kanezashi H, Kaler T, Schardl T B, Leiserson C E (2020) EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In: Proceedings of the thirty-fourth AAAI conference on artificial intelligence
Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’14. https://doi.org/10.1145/2623330.2623732. Association for Computing Machinery, New York, pp 701–710
Ribeiro LF, Saverese PH, Figueiredo DR (2017) Struc2vec: Learning node representations from structural identity. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’17. https://doi.org/10.1145/3097983.3098061. Association for Computing Machinery, New York, pp 385–394
Seo Y, Defferrard M, Vandergheynst P, Bresson X (2018) Structured sequence modeling with graph convolutional recurrent networks. In: Cheng L, Leung ACS, Ozawa S (eds) Neural information processing. Springer International Publishing, Cham, pp 362–373
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 ’15. https://doi.org/10.1145/2736277.2741093. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, pp 1067–1077
Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks. International Conference on Learning Representations. https://openreview.net/forum?id=rJXMpikCZ. Accepted as poster
Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1225–1234
Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI-18. International Joint Conferences on Artificial Intelligence Organization, pp 3634–3640. https://doi.org/10.24963/ijcai.2018/505
Funding
The authors would like to acknowledge the support provided by the National Natural Science Foundation of China under Grant 61872222, and the Young Scholars Program of Shandong University.
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhang, K., Pan, L. & Liu, S. A rating prediction model with cross projection and evolving GCN for bitcoin trading network. Pers Ubiquit Comput 27, 1561–1571 (2023). https://doi.org/10.1007/s00779-021-01650-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-021-01650-0