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Heterogeneous graph learning aims to capture complex relationships and diverse relational semantics among entities in a heterogeneous graph to obtain meaningful representations for nodes and edges.
metapath2vec: Scalable representation learning for heterogeneous networks
Y. Dong, N. V. Chawla, and A. Swami · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
W. L. Hamilton, Z. Ying, and J. Leskovec · 2017
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Graph attention networks
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, et al · 2018
Earlier work this paper cites.
Metapath-guided heterogeneous graph neural network for intent recommendation
S. Fan, J. Zhu, X. Han, C. Shi, L. Hu, B. Ma, and Y. Li · 2019
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Heterogeneous graph attention networks for semi-supervised short text classification
H. Linmei, T. Yang, C. Shi, H. Ji, and X. Li · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
N. Reimers and I. Gurevych · 2019
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Heterogeneous graph attention network
X. Wang, H. Ji, C. Shi, B. Wang, Y. Ye, P. Cui, and P. S. Yu · 2019
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Heterogeneous graph attention network
X. Wang, H. Ji, C. Shi, B. Wang, Y. Ye, et al · 2019
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Heterogeneous graph neural network
C. Zhang, D. Song, C. Huang, A. Swami, and N. V. Chawla · 2019
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MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding
X. Fu, J. Zhang, Z. Meng, and I. King · 2020
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Heterogeneous graph transformer
Z. Hu, Y. Dong, K. Wang, and Y. Sun · 2020
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Self-supervised auxiliary learning with meta-paths for heterogeneous graphs
D. Hwang, J. Park, S. Kwon, K. Kim, J.-W. Ha, and H. J. Kim · 2020
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Unsupervised attributed multiplex network embedding
C. Park, D. Kim, J. Han, and H. Yu · 2020
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Heterogeneous network representation learning: A unified framework with survey and benchmark
C. Yang, Y. Xiao, Y. Zhang, Y. Sun, and J. Han · 2020
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Heterogeneous graph neural network via attribute completion
D. Jin, C. Huo, C. Liang, and L. Yang · 2021
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Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks
Q. Lv, M. Ding, Q. Liu, Y. Chen, W. Feng, S. He, C. Zhou, J. Jiang, Y. Dong, and J. Tang · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, et al · 2021
Cited alongside, same era.
Hetegcn: heterogeneous graph convolutional networks for text classification
R. Ragesh, S. Sellamanickam, A. Iyer, R. Bairi, and V. Lingam · 2021
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Relational message passing for knowledge graph completion
H. Wang, H. Ren, and J. Leskovec · 2021
Cited alongside, same era.
Self-supervised learning of contextual embeddings for link prediction in heterogeneous networks
P. Wang, K. Agarwal, C. Ham, S. Choudhury, and C. K. Reddy · 2021
Cited alongside, same era.
Graphllm: Boosting graph reasoning ability of large language model
Z. Chai, T. Zhang, L. Wu, K. Han, X. Hu, X. Huang, and Y. Yang · 2023
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Heterogeneous graph contrastive learning for recommendation
M. Chen, C. Huang, L. Xia, W. Wei, Y. Xu, and R. Luo · 2023
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Exploring the potential of large language models (llms) in learning on graphs
Z. Chen, H. Mao, H. Li, et al · 2023
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Talk like a graph: Encoding graphs for large language models
B. Fatemi, J. Halcrow, and B. Perozzi · 2023
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Single-cell biological network inference using a heterogeneous graph transformer
A. Ma, X. Wang, J. Li, C. Wang, T. Xiao, Y. Liu, H. Cheng, J. Wang, Y. Li, Y. Chang, et al · 2023
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X. Wang, N. Liu, H. Han, and C. Shi · 2021
Cited alongside, same era.
Heterogeneous graph structure learning for graph neural networks
J. Zhao, X. Wang, C. Shi, B. Hu, G. Song, and Y. Ye · 2021
Cited alongside, same era.
Twhin: Embedding the twitter heterogeneous information network for personalized recommendation
A. El-Kishky, T. Markovich, S. Park, C. Verma, B. Kim, R. Eskander, Y. Malkov, F. Portman, S. Samaniego, Y. Xiao, et al · 2022
Cited alongside, same era.
X-goal: multiplex heterogeneous graph prototypical contrastive learning
B. Jing, S. Feng, Y. Xiang, X. Chen, Y. Chen, and H. Tong · 2022
Cited alongside, same era.
Generated knowledge prompting for commonsense reasoning
J. Liu, A. Liu, X. Lu, S. Welleck, P. West, R. L. Bras, Y. Choi, and H. Hajishirzi · 2022
Cited alongside, same era.
Rethinking the role of demonstrations: What makes in-context learning work?
S. Min, X. Lyu, A. Holtzman, M. Artetxe, M. Lewis, H. Hajishirzi, and L. Zettlemoyer · 2022
Cited alongside, same era.
A survey on heterogeneous graph embedding: methods, techniques, applications and sources
X. Wang, D. Bo, C. Shi, S. Fan, Y. Ye, and S. Y. Philip · 2022
Cited alongside, same era.
Later among the works it cites.
Representation learning with large language models for recommendation
X. Ren, W. Wei, L. Xia, L. Su, S. Cheng, J. Wang, D. Yin, and C. Huang · 2023
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Distilling reasoning capabilities into smaller language models
K. Shridhar, A. Stolfo, and M. Sachan · 2023
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Query-dependent prompt evaluation and optimization with offline inverse rl
H. Sun, A. Hüyük, and M. van der Schaar · 2023
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Graphgpt: Graph instruction tuning for large language models, 2023
J. Tang, Y. Yang, W. Wei, L. Shi, L. Su, S. Cheng, D. Yin, and C. Huang · 2023
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Heterogeneous graph masked autoencoders
Y. Tian, K. Dong, C. Zhang, C. Zhang, and N. V. Chawla · 2023
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Llmrec: Large language models with graph augmentation for recommendation
W. Wei, X. Ren, J. Tang, Q. Wang, L. Su, S. Cheng, J. Wang, D. Yin, and C. Huang · 2023
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Augmenting low-resource text classification with graph-grounded pre-training and prompting
Z. Wen and Y. Fang · 2023
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Knowledge enhancement for contrastive multi-behavior recommendation
H. Xuan, Y. Liu, B. Li, and H. Yin · 2023
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Tree of thoughts: Deliberate problem solving with large language models
S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan · 2023
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Natural language is all a graph needs
R. Ye, C. Zhang, R. Wang, S. Xu, and Y. Zhang · 2023
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