Farouk Damoun
ABSTRACT
Graph related tasks, such as graph classification and clustering, have been substantially improved with the advent of graph neural networks (GNNs). However, existing graph embedding models focus on homogeneous graphs that ignore the heterogeneity of the graphs. Therefore, using homogeneous graph embedding models on heterogeneous graphs discards the rich semantics of graphs and achieves average performance, especially by utilizing unlabeled information. However, limited work has been done on whole heterogeneous graph embedding as a supervised task. In light of this, we investigate unsupervised distributed representations learning on heterogeneous graphs and propose a novel model named G-HIN2Vec, Graph-Level Heterogeneous Information Network to Vector. Inspired by recent advances of unsupervised learning in natural language processing, G-HIN2Vec utilizes negative sampling technique as an unlabeled approach and learns graph embedding matrix from different pre-defined meta-paths. We conduct a variety of experiments on three main graph downstream applications on different socio-demographic cardholder features, graph regression, graph clustering, and graph classification, such as gender classification, age, and income prediction, which shows superior performance of our proposed GNN model on real-world financial credit card data.
- Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, and Wei Wang. 2019. Unsupervised inductive graph-level representation learning via graph-graph proximity. arXiv:1904.01098 (2019).Google Scholar
- Karsten M Borgwardt and Hans-Peter Kriegel. 2005. Shortest-path kernels on graphs. In Fifth IEEE international conference on data mining (ICDM'05). IEEE.Google ScholarDigital Library
- Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang. 2018. A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Transactions on Knowledge and Data Engineering 30, 9 (2018), 1616--1637.Google ScholarDigital Library
- Riccardo Di Clemente, Miguel Luengo-Oroz, Matias Travizano, Sharon Xu, Bapu Vaitla, and Marta C González. 2018. Sequences of purchases in credit card data reveal lifestyles in urban populations. Nature communications 9, 1 (2018), 1--8.Google Scholar
- Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 135--144.Google ScholarDigital Library
- Curtis E. Dyreson, William S. Evans, Hong Lin, and Richard T. Snodgrass. 2000. Efficiently supporting temporal granularities. IEEE Transactions on Knowledge and Data Engineering 12, 4 (2000), 568--587.Google ScholarDigital Library
- Tao-yang Fu, Wang-Chien Lee, and Zhen Lei. 2017. Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management.Google Scholar
- Palash Goyal and Emilio Ferrara. 2018. Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Systems 151 (2018), 78--94.Google ScholarCross Ref
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 855--864.Google ScholarDigital Library
- Anish Khazane, Jonathan Rider, Max Serpe, Antonia Gogoglou, Keegan Hines, C Bayan Bruss, and Richard Serpe. 2019. Deeptrax: Embedding graphs of financial transactions. In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 126--133.Google ScholarCross Ref
- Ziqi Liu, Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, and Le Song. 2018. Heterogeneous graph neural networks for malicious account detection. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2077--2085.Google ScholarDigital Library
- Ziqi Liu, Zhiqiang Wang, Yue Shen, Jian Ma, Wenliang Zhong, Jinjie Gu, Jun Zhou, Shuang Yang, et al. 2019. Graph representation learning for merchant incentive optimization in mobile payment marketing. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management.Google ScholarDigital Library
- Guixiang Ma, Nesreen K Ahmed, Theodore L Willke, and Philip S Yu. 2021. Deep graph similarity learning: A survey. Data Mining and Knowledge Discovery 35, 3 (2021), 688--725.Google ScholarCross Ref
- Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal. 2017. graph2vec: Learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 (2017).Google Scholar
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 701--710.Google ScholarDigital Library
- Susie Xi Rao, Shuai Zhang, Zhichao Han, Zitao Zhang, Wei Min, Zhiyao Chen, Yinan Shan, Yang Zhao, and Ce Zhang. 2020. xFraud: Explainable fraud transaction detection on heterogeneous graphs. arXiv preprint arXiv:2011.12193 (2020).Google Scholar
- Yuxiang Ren, Hao Zhu, Jiawei Zhang, Peng Dai, and Liefeng Bo. 2021. Ensemfdet: An ensemble approach to fraud detection based on bipartite graph. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2039--2044.Google ScholarCross Ref
- Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt. 2011. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research 12, 9 (2011).Google Scholar
- Nino Shervashidze, SVN Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten Borgwardt. 2009. Efficient graphlet kernels for large graph comparison. In Artificial intelligence and statistics. PMLR, 488--495.Google Scholar
- Lichao Sun, Lifang He, Zhipeng Huang, Bokai Cao, Congying Xia, Xiaokai Wei, and S Yu Philip. 2018. Joint embedding of meta-path and meta-graph for heterogeneous information networks. In 2018 IEEE international conference on big knowledge (ICBK). IEEE.Google ScholarCross Ref
- Susheel Suresh, Vinith Budde, Jennifer Neville, Pan Li, and Jianzhu Ma. 2021. Breaking the limit of graph neural networks by improving the assortativity of graphs with local mixing patterns. arXiv preprint arXiv:2106.06586 (2021).Google Scholar
- S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt. 2010. Graph kernels. Journal of Machine Learning Research 11 (2010), 1201--1242.Google ScholarCross Ref
- Faqiang Wang, Wangmeng Zuo, Liang Lin, David Zhang, and Lei Zhang. 2016. Joint learning of single-image and cross-image representations for person re-identification. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1288--1296.Google ScholarCross Ref
- Jianian Wang, Sheng Zhang, Yanghua Xiao, and Rui Song. 2021. A review on graph neural network methods in financial applications. arXiv preprint arXiv:2111.15367 (2021).Google Scholar
- Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, and Philip S Yu. 2020. A survey on heterogeneous graph embedding: methods, techniques, applications and sources. arXiv preprint arXiv:2011.14867 (2020).Google Scholar
- Yueyang Wang, Ziheng Duan, Binbing Liao, Fei Wu, and Yueting Zhuang. 2019. Heterogeneous attributed network embedding with graph convolutional networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33.Google ScholarDigital Library
- Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems (2020).Google ScholarCross Ref
- Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018).Google Scholar
- Pinar Yanardag and SVN Vishwanathan. 2015. Deep graph kernels. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 1365--1374.Google ScholarDigital Library
- Carl Yang, Yuxin Xiao, Yu Zhang, Yizhou Sun, and Jiawei Han. 2020. Heterogeneous network representation learning: A unified framework with survey and benchmark. IEEE Transactions on Knowledge and Data Engineering (2020).Google ScholarDigital Library
- Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. Advances in neural information processing systems 31 (2018).Google Scholar
- Yizhen Zheng, Vincent Lee, Zonghan Wu, and Shirui Pan. 2021. Heterogeneous Graph Attention Network for Small and Medium-Sized Enterprises Bankruptcy Prediction. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 140--151.Google Scholar
Index Terms
- G-HIN2Vec: Distributed heterogeneous graph representations for cardholder transactions
Recommendations
Multi-view Self-supervised Heterogeneous Graph Embedding
Machine Learning and Knowledge Discovery in Databases. Research TrackAbstractGraph mining tasks often suffer from the lack of supervision from labeled information due to the intrinsic sparseness of graphs and the high cost of manual annotation. To alleviate this issue, inspired by recent advances of self-supervised ...
Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications
AbstractGraph Neural Networks (GNNs) have achieved excellent performance of graph representation learning and attracted plenty of attentions in recent years. Most of GNNs aim to learn embedding vectors of the homogeneous graph which only contains single ...
Transformation graph G-+-
The transformation graph G^-^+^- of a graph G is the graph with vertex set V(G)@?E(G), in which two vertices u and v are joined by an edge if one of the following conditions holds: (i) u,v@?V(G) and they are not adjacent in G, (ii) u,v@?E(G) and they ...
Comments