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Inspired by the success of large language models, there is a trend toward developing graph foundation models to conduct diverse downstream tasks in various domains.
Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., and Wolf, T · 1910
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Distinguishing enzyme structures from non-enzymes without alignments
Dobson, P. D. and Doig, A. J · 2003
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Protein function prediction via graph kernels
Borgwardt, K. M., Ong, C. S., Schönauer, S., Vishwanathan, S., Smola, A. J., and Kriegel, H.-P · 2005
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To transfer or not to transfer
Rosenstein, M. T., Marx, Z., Kaelbling, L. P., and Dietterich, T. G · 2005
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Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
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Social influence analysis in large-scale networks
Tang, J., Sun, J., Wang, C., and Yang, Z · 2009
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Labeling trick: A theory of using graph neural networks for multi-node representation learning
Zhang, M., Li, P., Xia, Y., Wang, K., and Jin, L · 2010
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Subgraph matching kernels for attributed graphs
Kriege, N. and Mutzel, P · 2012
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Query-driven active surveying for collective classification
Namata, G., London, B., Getoor, L., Huang, B., and Edu, U · 2012
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Translating embeddings for modeling multi-relational data
Bordes, A., García-Durán, A., Weston, J., and Yakhnenko, O · 2013
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Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
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A Semantic Matching Energy Function for Learning with Multi-relational Data
Bordes, A., Glorot, X., Weston, J., and Bengio, Y · 2014
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The network data repository with interactive graph analytics and visualization
Rossi, R. and Ahmed, N · 2015
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Observed versus latent features for knowledge base and text inference
Toutanova, K. and Chen, D · 2015
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Deep graph kernels
Yanardag, P. and Vishwanathan, S · 2015
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Embedding entities and relations for learning and inference in knowledge bases
Yang, B., Yih, W., He, X., Gao, J., and Deng, L · 2015
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, Z., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Knowledge graph embedding: A survey of approaches and applications
Wang, Q., Mao, Z., Wang, B., and Guo, L · 2017
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Deeppath: A reinforcement learning method for knowledge graph reasoning
Xiong, W., Hoang, T., and Wang, W. Y · 2017
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Convolutional 2d knowledge graph embeddings
Dettmers, T., Minervini, P., Stenetorp, P., and Riedel, S · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M., Lee, K., and Toutanova, K · 2018
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Modeling relational data with graph convolutional networks
Schlichtkrull, M. S., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., and Welling, M · 2018
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Geom-gcn: Geometric graph convolutional networks
Pei, H., Wei, B., Chang, K. C.-C., Lei, Y., and Yang, B · 2019
Cited alongside, same era.
DRUM: end-to-end differentiable rule mining on knowledge graphs
Sadeghian, A., Armandpour, M., Ding, P., and Wang, D. Z · 2019
Cited alongside, same era.
Rotate: Knowledge graph embedding by relational rotation in complex space
Sun, Z., Deng, Z., Nie, J., and Tang, J · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., and Weinberger, K · 2019
Cited alongside, same era.
Low-dimensional hyperbolic knowledge graph embeddings
Chami, I., Wolf, A., Juan, D.-C., Sala, F., Ravi, S., and Ré, C · 2020
Cited alongside, same era.
Principal neighbourhood aggregation for graph nets
Corso, G., Cavalleri, L., Beaini, D., Liò, P., and Velickovic, P · 2020
Cited alongside, same era.
Double permutation equivariance for knowledge graph completion
Gao, J., Zhou, Y., and Ribeiro, B · 2023
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Enhancing graph neural networks with structure-based prompt
Ge, Q., Zhao, Z., Liu, Y., Cheng, A., Li, X., Wang, S., and Yin, D · 2023
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Relational message passing for fully inductive knowledge graph completion
Geng, Y., Chen, J., Pan, J. Z., Chen, M., Jiang, S., Zhang, W., and Chen, H · 2023
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Prompt Tuning for Multi-View Graph Contrastive Learning
Gong, C., Li, X., Yu, J., Yao, C., Tan, J., Yu, C., and Yin, D · 2023
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A theory of link prediction via relational weisfeiler-leman on knowledge graphs
Huang, X., Romero, M., Ceylan, İ. İ., and Barceló, P · 2023
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Codex: A comprehensive knowledge graph completion benchmark
Safavi, T. and Koutra, D · 2020
Cited alongside, same era.
Mpnet: Masked and permuted pre-training for language understanding
Song, K., Tan, X., Qin, T., Lu, J., and Liu, T · 2020
Cited alongside, same era.
Inductive relation prediction by subgraph reasoning
Teru, K. K., Denis, E. G., and Hamilton, W. L · 2020
Cited alongside, same era.
Composition-based multi-relational graph convolutional networks
Vashishth, S., Sanyal, S., Nitin, V., and Talukdar, P. P · 2020
Cited alongside, same era.
Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers
Wang, W., Wei, F., Dong, L., Bao, H., Yang, N., and Zhou, M · 2020
Cited alongside, same era.
Topology-aware correlations between relations for inductive link prediction in knowledge graphs
Chen, J., He, H., Wu, F., and Wang, J · 2021
Cited alongside, same era.
InGram: Inductive knowledge graph embedding via relation graphs
Lee, J., Chung, C., and Whang, J. J · 2023
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All in one: Multi-task prompting for graph neural networks
Sun, X., Cheng, H., Li, J., Liu, B., and Guan, J · 2023
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Virtual Node Tuning for Few-shot Node Classification
Tan, Z., Guo, R., Ding, K., and Liu, H · 2023
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Adaprop: Learning adaptive propagation for graph neural network based knowledge graph reasoning
Zhang, Y., Zhou, Z., Yao, Q., Chu, X., and Han, B · 2023
Later among the works it cites.
An ood multi-task perspective for link prediction with new relation types and nodes
Zhou, J., Bevilacqua, B., and Ribeiro, B · 2023
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A*net: A scalable path-based reasoning approach for knowledge graphs
Zhu, Z., Yuan, X., Galkin, M., Xhonneux, L. A. C., Zhang, M., Gazeau, M., and Tang, J · 2023
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A prompt-based knowledge graph foundation model for universal in-context reasoning
Cui, Y., Sun, Z., and Hu, W · 2024
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Zerog: Investigating cross-dataset zero-shot transferability in graphs
Li, Y., Wang, P., Li, Z., Yu, J. X., and Li, J · 2024
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One for all: Towards training one graph model for all classification tasks
Liu, H., Feng, J., Kong, L., Liang, N., Tao, D., Chen, Y., and Zhang, M · 2024
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Mao, H., Chen, Z., Tang, W., Zhao, J., Ma, Y., Zhao, T., Shah, N., Galkin, M., and Tang, J · 2024
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Higpt: Heterogeneous graph language model, 2024
Tang, J., Yang, Y., Wei, W., Shi, L., Xia, L., Yin, D., and Huang, C · 2024
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LLM as prompter: Low-resource inductive reasoning on arbitrary knowledge graphs
Wang, K., Xu, Y., Wu, Z., and Luo, S · 2024
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Graph percolation embeddings for efficient knowledge graph inductive reasoning
Wang, K., Lin, D., and Luo, S · 2024
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Anygraph: Graph foundation model in the wild, 2024
Xia, L. and Huang, C · 2024
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Opengraph: Towards open graph foundation models, 2024
Xia, L., Kao, B., and Huang, C · 2024
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Language is all a graph needs, 2024
Ye, R., Zhang, C., Wang, R., Xu, S., and Zhang, Y · 2024
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Graphany: A foundation model for node classification on any graph, 2024
Zhao, J., Mostafa, H., Galkin, M., Bronstein, M., Zhu, Z., and Tang, J · 2024
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Prog: A graph prompt learning benchmark
Zi, C., Zhao, H., Sun, X., Lin, Y., Cheng, H., and Li, J · 2024
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TRIX: A more expressive model for zero-shot domain transfer in knowledge graphs
Zhang, Y., Bevilacqua, B., Galkin, M., and Ribeiro, B · 2025
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