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Node-dependent Semantic Search over Heterogeneous Graph Neural Networks

Published: 21 October 2023 Publication History

Abstract

In recent years, Heterogeneous Graph Neural Networks (HGNNs) have been the state-of-the-art approaches for various tasks on Heterogeneous Graphs (HGs), e.g., recommendation and social network analysis. Despite the success of existing HGNNs, the utilization of the intricate semantic information in HGs is still insufficient. In this work, we study the problem of how to design powerful HGNNs under the guidance of node-dependent semantics. Specifically, to perform semantic search over HGNNs, we propose to develop semantic structures in terms of relation selection and connection selection, which could guide a task-relevant message flow. Furthermore, to better capture the diversified property of different node samples in HGs, we design predictors to adaptively decide the semantic structures per node. Extensive experiments on seven benchmarking datasets across different downstream tasks, i.e., node classification and recommendation, show that our method can consistently outperform various state-of-the-art baselines with shorter inference latency, which justifies its effectiveness and efficiency. The code and data are available at https://github.com/BUPT-GAMMA/NDS.

References

[1]
Ye Bi, Liqiang Song, Mengqiu Yao, Zhenyu Wu, Jianming Wang, and Jing Xiao. 2020. A heterogeneous information network based cross domain insurance recommendation system for cold start users. In SIGIR. 2211--2220.
[2]
Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, Jianxin Li, and Philip S Yu. 2021. Knowledge-preserving incremental social event detection via heterogeneous gnns. In TheWebConf. 3383--3395.
[3]
Jiamin Chen, Jianliang Gao, Yibo Chen, Moctard Babatounde Oloulade, Tengfei Lyu, and Zhao Li. 2021. Graphpas: Parallel architecture search for graph neural networks. In SIGIR. 2182--2186.
[4]
Jingfan Chen, Guanghui Zhu, Haojun Hou, Chunfeng Yuan, and Yihua Huang. 2022. AutoGSR: Neural architecture search for graph-based session recommendation. In SIGIR. 1694--1704.
[5]
An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei, and Min Sun. 2020. Instanas: Instance-aware neural architecture search. In AAAI. 3577--3584.
[6]
Yuhui Ding, Quanming Yao, Huan Zhao, and Tong Zhang. 2021. DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks. In KDD. 279--288.
[7]
Yuan Fang, Wenqing Lin, Vincent W Zheng, Min Wu, Kevin Chen-Chuan Chang, and Xiao-Li Li. 2016. Semantic proximity search on graphs with metagraph-based learning. In ICDE. 277--288.
[8]
Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. 2020. Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In TheWebConf. 2331--2341.
[9]
Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu. 2020. Graph Neural Architecture Search. In IJCAI. 1403--1409.
[10]
Yang Gao, Peng Zhang, Zhao Li, Chuan Zhou, Yongchao Liu, and Yue Hu. 2021. Heterogeneous Graph Neural Architecture Search. In ICDM. 1066--1071.
[11]
Floris Geerts, Filip Mazowiecki, and Guillermo Perez. 2021. Let's agree to degree: Comparing graph convolutional networks in the message-passing framework. In ICML. 3640--3649.
[12]
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In ICML. 1263--1272.
[13]
Weili Guan, Fangkai Jiao, Xuemeng Song, Haokun Wen, Chung-Hsing Yeh, and Xiaojun Chang. 2022. Personalized Fashion Compatibility Modeling via Metapath-guided Heterogeneous Graph Learning. In SIGIR. 482--491.
[14]
Xiaotian Han, Chuan Shi, Senzhang Wang, S Yu Philip, and Li Song. 2018. Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks. In IJCAI. 3393--3399.
[15]
Yizeng Han, Gao Huang, Shiji Song, Le Yang, Honghui Wang, and Yulin Wang. 2021. Dynamic neural networks: A survey. arXiv preprint arXiv:2102.04906 (2021).
[16]
Zhenyu Han, Fengli Xu, Jinghan Shi, Yu Shang, Haorui Ma, Pan Hui, and Yong Li. 2020. Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network. In CIKM. 455--464.
[17]
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. 2020. Heterogeneous graph transformer. In TheWebConf. 2704--2710.
[18]
Zhao Huan, Yao Quanming, and Tu Weiwei. 2021. Search to aggregate neighborhood for graph neural network. In ICDE. 552--563.
[19]
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, and Kilian Q Weinberger. 2018. Multi-scale dense networks for resource efficient image classification. In ICLR.
[20]
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017. Densely connected convolutional networks. In CVPR. 4700--4708.
[21]
Zhipeng Huang, Yudian Zheng, Reynold Cheng, Yizhou Sun, Nikos Mamoulis, and Xiang Li. 2016. Meta structure: Computing relevance in large heterogeneous information networks. In KDD. 1595--1604.
[22]
Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Weinan Zhang, Yong Yu, Zheng Zhang, and Alexander J Smola. 2020. An efficient neighborhood-based interaction model for recommendation on heterogeneous graph. In KDD. 75--84.
[23]
Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR.
[24]
Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.
[25]
Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou, and Xia Hu. 2020. Policy-gnn: Aggregation optimization for graph neural networks. In KDD. 461--471.
[26]
Chao Li, Hao Xu, and Kun He. 2022. Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks. arXiv preprint arXiv:2211.14752 (2022).
[27]
Dongyue Li, Tao Yang, Lun Du, Zhezhi He, and Li Jiang. 2021. AdaptiveGCN: Efficient GCN Through Adaptively Sparsifying Graphs. In CIKM. 3206--3210.
[28]
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. 2019. Deepgcns: Can gcns go as deep as cnns?. In ICCV. 9267--9276.
[29]
Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2019. Darts: Differentiable architecture search. In ICLR.
[30]
Siwei Liu, Iadh Ounis, Craig Macdonald, and Zaiqiao Meng. 2020. A heterogeneous graph neural model for cold-start recommendation. In SIGIR. 2029--2032.
[31]
Zemin Liu, Yuan Fang, Chenghao Liu, and Steven CH Hoi. 2021. Node-wise localization of graph neural networks. In IJCAI.
[32]
Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, and Jie Tang. 2021. Are we really making much progress? Revisiting, benchmarking and refining heterogeneous graph neural networks. In KDD. 1150--1160.
[33]
Xiaojun Ma, Junshan Wang, Hanyue Chen, and Guojie Song. 2021. Improving Graph Neural Networks with Structural Adaptive Receptive Fields. In TheWebConf. 2438--2447.
[34]
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In ESWC. Springer, 593--607.
[35]
Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, and S Yu Philip. 2016. A survey of heterogeneous information network analysis. IEEE Transactions on Knowledge and Data Engineering, Vol. 29, 1 (2016), 17--37.
[36]
Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S Yu, and Tianyi Wu. 2011. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. In VLDB. 992--1003.
[37]
Surat Teerapittayanon, Bradley McDanel, and Hsiang-Tsung Kung. 2016. Branchynet: Fast inference via early exiting from deep neural networks. In ICPR. 2464--2469.
[38]
Andreas Veit and Serge Belongie. 2018. Convolutional networks with adaptive inference graphs. In ECCV. 3--18.
[39]
Petar Velivc ković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In ICLR.
[40]
Chi Wang, Rajat Raina, David Fong, Ding Zhou, Jiawei Han, and Greg Badros. 2011. Learning relevance from heterogeneous social network and its application in online targeting. In SIGIR. 655--664.
[41]
Weiqing Wang, Hongzhi Yin, Xingzhong Du, Wen Hua, Yongjun Li, and Quoc Viet Hung Nguyen. 2019b. Online user representation learning across heterogeneous social networks. In SIGIR. 545--554.
[42]
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).
[43]
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu. 2019a. Heterogeneous graph attention network. In TheWebconf. 2022--2032.
[44]
Zhen Wang, Zhewei Wei, Yaliang Li, Weirui Kuang, and Bolin Ding. 2022. Graph Neural Networks with Node-wise Architecture. In KDD. 1949--1958.
[45]
Lanning Wei, Zhiqiang He, Huan Zhao, and Quanming Yao. 2023. Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification. In TheWebConf. 588--598.
[46]
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, Vol. 32, 1 (2020), 4--24.
[47]
Sirui Xie, Hehui Zheng, Chunxiao Liu, and Liang Lin. 2019. SNAS: stochastic neural architecture search. In ICLR.
[48]
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks?. In ICLR.
[49]
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. In ICML. 5453--5462.
[50]
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).
[51]
Jiaxuan You, Zhitao Ying, and Jure Leskovec. 2020. Design space for graph neural networks. In NeurIPS. 17009--17021.
[52]
Tan Yu, Yi Yang, Yi Li, Lin Liu, Hongliang Fei, and Ping Li. 2021. Heterogeneous attention network for effective and efficient cross-modal retrieval. In SIGIR. 1146--1156.
[53]
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim. 2019. Graph transformer networks. In NeurIPS. 11983--11993.
[54]
Jiani Zhang, Xingjian Shi, Shenglin Zhao, and Irwin King. 2019. Star-gcn: Stacked and reconstructed graph convolutional networks for recommender systems. In IJCAI.
[55]
Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. In NeurIPS.
[56]
Wentao Zhang, Mingyu Yang, Zeang Sheng, Yang Li, Wen Ouyang, Yangyu Tao, Zhi Yang, and Bin Cui. 2021. Node Dependent Local Smoothing for Scalable Graph Learning. In NeurIPS. 20321--20332.
[57]
Huan Zhao, Quanming Yao, Jianda Li, Yangqiu Song, and Dik Lun Lee. 2017. Meta-graph based recommendation fusion over heterogeneous information networks. In KDD. 635--644.
[58]
Jun Zhao, Zhou Zhou, Ziyu Guan, Wei Zhao, Wei Ning, Guang Qiu, and Xiaofei He. 2019. Intentgc: a scalable graph convolution framework fusing heterogeneous information for recommendation. In KDD. 2347--2357.
[59]
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2020. Graph neural networks: A review of methods and applications. AI Open, Vol. 1 (2020), 57--81.

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  • (2024)StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information NetworksApplied Sciences10.3390/app1402093514:2(935)Online publication date: 22-Jan-2024

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    cover image ACM Conferences
    CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
    October 2023
    5508 pages
    ISBN:9798400701245
    DOI:10.1145/3583780
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    Published: 21 October 2023

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    Author Tags

    1. heterogeneous graph neural networks
    2. neural architecture search
    3. node-dependent semantic search

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    • (2024)StructSim: Meta-Structure-Based Similarity Measure in Heterogeneous Information NetworksApplied Sciences10.3390/app1402093514:2(935)Online publication date: 22-Jan-2024

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