AGHE - Approach for Generating Enhanced Heterogeneous Embeddings from Heterogeneous Graphs
Resumo
Embeddings represent a viable solution to address the challenge of data and information generation in Heterogeneous Graphs. This paper presents the approach for generating and processing heterogeneous embeddings (AGHE), which are built from various data types such as text, images, and subgraphs embedded in nodes. The AGHE comprises several steps, from graph creation to generating embeddings using metapaths and aggregating information from neighboring nodes. The experiments conducted investigated the performance of Recommender System tasks applied to the generated embeddings: node-local text-based, neighbor-aggregated text-based, metapath-based, and text and metapath composition. Results underscore the effectiveness in representing data heterogeneity in Deep Learning systems based on Heterogeneous Graph.
Referências
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Rozemberczki, B., Davies, R., Sarkar, R., and Sutton, C. (2020). Gemsec: Graph embedding with self clustering. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’19, page 65–72, New York, NY, USA. Association for Computing Machinery.
Shi, C., Zhang, Z., Luo, P., Yu, P. S., Yue, Y., and Wu, B. (2015). Semantic path based personalized recommendation on weighted heterogeneous information networks. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM ’15, page 453–462, New York, NY, USA. Association for Computing Machinery.
Sun, Y. and Han, J. (2012). Mining Heterogeneous Information Networks: Principles and Methodologies. Morgan amp; Claypool Publishers.
Wang, H., Wang, N., and Yeung, D.-Y. (2015). Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15, page 1235–1244, New York, NY, USA. Association for Computing Machinery.
Wang, X., Bo, D., Shi, C., Fan, S., Ye, Y., and Yu, P. S. (2023). A survey on heterogeneous graph embedding: Methods, techniques, applications and sources. IEEE Transactions on Big Data, 9(2):415–436.
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., and Yu, P. S. (2019). Heterogeneous graph attention network. In The World Wide Web Conference, WWW ’19, page 2022–2032, New York, NY, USA. Association for Computing Machinery.
Wu, S., Sun, F., Zhang, W., Xie, X., and Cui, B. (2022). Graph neural networks in recommender systems: A survey. ACM Comput. Surv., 55(5).
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S. (2019). A comprehensive survey on graph neural networks. CoRR, abs/1901.00596.
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J. (2018a). Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery amp; Data Mining, KDD ’18, page 974–983, New York, NY, USA. Association for Computing Machinery.
Ying, R., You, J., Morris, C., Ren, X., Hamilton, W. L., and Leskovec, J. (2018b). Hierarchical graph representation learning with differentiable pooling. CoRR, abs/1806.08804.
Zhang, C., Song, D., Huang, C., Swami, A., and Chawla, N. V. (2019). Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery amp; Data Mining, KDD ’19, page 793–803, New York, NY, USA. Association for Computing Machinery.
Zhiyuan Liu, J. Z. (2020). Introduction to Graph Neural Networks. Springer Cham.
Bank, D., Koenigstein, N., and Giryes, R. (2020). Autoencoders. CoRR, abs/2003.05991.
Bank, D., Koenigstein, N., and Giryes, R. (2021). Autoencoders.
Dong, Y., Chawla, N. V., and Swami, A. (2017). Metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’17, page 135–144, New York, NY, USA. Association for Computing Machinery.
Fu, X., Zhang, J., Meng, Z., and King, I. (2020). Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In Proceedings of The Web Conference 2020, WWW ’20, page 2331–2341, New York, NY, USA. Association for Computing Machinery.
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E. (2017). Neural message passing for quantum chemistry.
Hamilton, W. L., Ying, R., and Leskovec, J. (2017a). Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, page 1025–1035, Red Hook, NY, USA. Curran Associates Inc.
Hamilton, W. L., Ying, R., and Leskovec, J. (2017b). Inductive representation learning on large graphs. CoRR, abs/1706.02216.
Hamilton, W. L., Ying, R., and Leskovec, J. (2017c). Representation learning on graphs: Methods and applications. CoRR, abs/1709.05584.
Han, K., Wang, Y., Guo, J., Tang, Y., and Wu, E. (2022). Vision gnn: An image is worth graph of nodes. In Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., and Oh, A., editors, Advances in Neural Information Processing Systems, volume 35, pages 8291–8303. Curran Associates, Inc.
Jin, D., Huo, C., Liang, C., and Yang, L. (2021). Heterogeneous graph neural network via attribute completion. In Proceedings of the Web Conference 2021, WWW ’21, page 391–400, New York, NY, USA. Association for Computing Machinery.
Kipf, T. N. and Welling, M. (2016). Variational graph auto-encoders.
Perozzi, B., Al-Rfou, R., and Skiena, S. (2014). DeepWalk. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM.
Rozemberczki, B., Davies, R., Sarkar, R., and Sutton, C. (2020). Gemsec: Graph embedding with self clustering. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM ’19, page 65–72, New York, NY, USA. Association for Computing Machinery.
Shi, C., Zhang, Z., Luo, P., Yu, P. S., Yue, Y., and Wu, B. (2015). Semantic path based personalized recommendation on weighted heterogeneous information networks. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM ’15, page 453–462, New York, NY, USA. Association for Computing Machinery.
Sun, Y. and Han, J. (2012). Mining Heterogeneous Information Networks: Principles and Methodologies. Morgan amp; Claypool Publishers.
Wang, H., Wang, N., and Yeung, D.-Y. (2015). Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15, page 1235–1244, New York, NY, USA. Association for Computing Machinery.
Wang, X., Bo, D., Shi, C., Fan, S., Ye, Y., and Yu, P. S. (2023). A survey on heterogeneous graph embedding: Methods, techniques, applications and sources. IEEE Transactions on Big Data, 9(2):415–436.
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., and Yu, P. S. (2019). Heterogeneous graph attention network. In The World Wide Web Conference, WWW ’19, page 2022–2032, New York, NY, USA. Association for Computing Machinery.
Wu, S., Sun, F., Zhang, W., Xie, X., and Cui, B. (2022). Graph neural networks in recommender systems: A survey. ACM Comput. Surv., 55(5).
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Yu, P. S. (2019). A comprehensive survey on graph neural networks. CoRR, abs/1901.00596.
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J. (2018a). Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery amp; Data Mining, KDD ’18, page 974–983, New York, NY, USA. Association for Computing Machinery.
Ying, R., You, J., Morris, C., Ren, X., Hamilton, W. L., and Leskovec, J. (2018b). Hierarchical graph representation learning with differentiable pooling. CoRR, abs/1806.08804.
Zhang, C., Song, D., Huang, C., Swami, A., and Chawla, N. V. (2019). Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery amp; Data Mining, KDD ’19, page 793–803, New York, NY, USA. Association for Computing Machinery.
Zhiyuan Liu, J. Z. (2020). Introduction to Graph Neural Networks. Springer Cham.
Publicado
21/07/2024
Como Citar
ANGONESE, Silvio F.; GALANTE, Renata.
AGHE - Approach for Generating Enhanced Heterogeneous Embeddings from Heterogeneous Graphs. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 51. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 252-263.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2024.3051.
