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Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain, aiming to develop graph models trained on extensive and diverse data to enhance their applicability across various tasks and domains.
A new status index derived from sociometric analysis
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Matching, euler tours and the chinese postman
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The strength of weak ties
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Statistical mechanics of complex networks
Albert, R. and Barabási, A.-L · 2002
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Simrank: a measure of structural-context similarity
Jeh, G. and Widom, J · 2002
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Network motifs: simple building blocks of complex networks
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., and Alon, U · 2002
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Friends and neighbors on the web
Adamic, L. A. and Adar, E · 2003
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Learning from labeled and unlabeled data: An empirical study across techniques and domains
Chawla, N. V. and Karakoulas, G · 2005
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Self-supervised learning on graphs: Deep insights and new direction
Jin, W., Derr, T., Liu, H., Wang, Y., Wang, S., Liu, Z., and Tang, J · 2006
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An introduction to exponential random graph (p*) models for social networks
Robins, G., Pattison, P., Kalish, Y., and Lusher, D · 2007
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Mixed membership stochastic blockmodels
Airoldi, E. M., Blei, D., Fienberg, S., and Xing, E · 2008
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Strategies for network motifs discovery
Ribeiro, P., Silva, F., and Kaiser, M · 2009
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Kronecker graphs: an approach to modeling networks
Leskovec, J., Chakrabarti, D., Kleinberg, J., Faloutsos, C., and Ghahramani, Z · 2010
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Graph kernels
Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., and Borgwardt, K. M · 2010
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A combinatorial approach to graphlet counting
Hočevar, T. and Demšar, J · 2014
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SNAP Datasets: Stanford large network dataset collection
Leskovec, J. and Krevl, A · 2014
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Triadic closure pattern analysis and prediction in social networks
Huang, H., Tang, J., Liu, L., Luo, J., and Fu, X · 2015
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The network data repository with interactive graph analytics and visualization
Rossi, R. A. and Ahmed, N. K · 2015
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Neural bellman-ford networks: A general graph neural network framework for link prediction
Zhu, Z., Zhang, Z., Xhonneux, L.-P., and Tang, J · 2015
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Higher-order organization of complex networks
Benson, A. R., Gleich, D. F., and Leskovec, J · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Variational graph auto-encoders
Kipf, T. N. and Welling, M · 2016
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Aminer: Toward understanding big scholar data
Tang, J · 2016
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Structural diversity and homophily: A study across more than one hundred big networks
Dong, Y., Johnson, R. A., Xu, J., and Chawla, N. V · 2017
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Neural discrete representation learning
Van Den Oord, A., Vinyals, O., et al · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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Network structure inference, a survey: Motivations, methods, and applications
Brugere, I., Gallagher, B., and Berger-Wolf, T. Y · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Klicpera, J., Bojchevski, A., and Günnemann, S · 2018
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Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M · 2018
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Veličković, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D · 2018
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Graph convolutional neural networks for web-scale recommender systems
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., and Leskovec, J · 2018
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Link prediction based on graph neural networks
Zhang, M. and Chen, Y · 2018
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Fast graph representation learning with pytorch geometric
Fey, M. and Lenssen, J. E · 2019
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Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2019
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Structural transition in social networks
Murase, Y., Jo, H. H., Török, J., Kertész, J., Kaski, K., et al · 2019
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Graph neural networks exponentially lose expressive power for node classification
Oono, K. and Suzuki, T · 2019
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Deep Learning for the Life Sciences
Ramsundar, B., Eastman, P., Walters, P., Pande, V., Leswing, K., and Wu, Z · 2019
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On the equivalence between positional node embeddings and structural graph representations
Srinivasan, B. and Ribeiro, B · 2019
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Sun, F.-Y., Hoffmann, J., Verma, V., and Tang, J · 2019
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Composition-based multi-relational graph convolutional networks
Vashishth, S., Sanyal, S., Nitin, V., and Talukdar, P · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Position-aware graph neural networks
You, J., Ying, R., and Leskovec, J · 2019
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Graphsaint: Graph sampling based inductive learning method
Zeng, H., Zhou, H., Srivastava, A., Kannan, R., and Prasanna, V · 2019
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Link prediction via higher-order motif features
AbuOda, G., De Francisci Morales, G., and Aboulnaga, A · 2020
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The logical expressiveness of graph neural networks
Barceló, P., Kostylev, E. V., Monet, M., Pérez, J., Reutter, J., and Silva, J. P · 2020
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Networks beyond pairwise interactions: Structure and dynamics
Battiston, F., Cencetti, G., Iacopini, I., Latora, V., Lucas, M., Patania, A., Young, J.-G., and Petri, G · 2020
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A note on over-smoothing for graph neural networks
Cai, C. and Wang, Y · 2020
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Contrastive multi-view representation learning on graphs
Hassani, K. and Khasahmadi, A. H · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
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The homophily principle in social network analysis
Khanam, K. Z., Srivastava, G., and Mago, V · 2020
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A survey on graph kernels
Kriege, N. M., Johansson, F. D., and Morris, C · 2020
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A First Course in Network Science
Menczer, F., Fortunato, S., and Davis, C. A · 2020
Cited alongside, same era.
Tudataset: A collection of benchmark datasets for learning with graphs
Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M · 2020
Cited alongside, same era.
What can neural networks reason about?
Xu, K., Li, J., Zhang, M., Du, S. S., Kawarabayashi, K.-i., and Jegelka, S · 2020
Cited alongside, same era.
Graph contrastive learning with augmentations
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., and Shen, Y · 2020
Cited alongside, same era.
On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al · 2021
Cited alongside, same era.
Adaptive universal generalized pagerank graph neural network
When to pre-train graph neural networks? an answer from data generation perspective!
Cao, Y., Xu, J., Yang, C., Wang, J., Zhang, Y., Wang, C., Chen, L., and Yang, Y · 2023
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Graphllm: Boosting graph reasoning ability of large language model
Chai, Z., Zhang, T., Wu, L., Han, K., Hu, X., Huang, X., and Yang, Y · 2023
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How does over-squashing affect the power of gnns?
Di Giovanni, F., Rusch, T. K., Bronstein, M. M., Deac, A., Lackenby, M., Mishra, S., and Veličković, P · 2023
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Faith and fate: Limits of transformers on compositionality
Dziri, N., Lu, X., Sclar, M., Li, X. L., Jian, L., Lin, B. Y., West, P., Bhagavatula, C., Bras, R. L., Hwang, J. D., et al · 2023
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Chien, E., Peng, J., Li, P., and Milenkovic, O · 2021
Cited alongside, same era.
Gnnautoscale: Scalable and expressive graph neural networks via historical embeddings
Fey, M., Lenssen, J. E., Weichert, F., and Leskovec, J · 2021
Cited alongside, same era.
Malnet: A large-scale image database of malicious software
Freitas, S., Duggal, R., and Chau, D. H · 2021
Cited alongside, same era.
Pre-trained models: Past, present and future
Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al · 2021
Cited alongside, same era.
Joint subgraph-to-subgraph transitions: Generalizing triadic closure for powerful and interpretable graph modeling
Hibshman, J. I., Gonzalez, D., Sikdar, S., and Weninger, T · 2021
Cited alongside, same era.
Rethinking graph transformers with spectral attention
Kreuzer, D., Beaini, D., Hamilton, W., Létourneau, V., and Tossou, P · 2021
Cited alongside, same era.
Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
Lim, D., Hohne, F., Li, X., Huang, S. L., Gupta, V., Bhalerao, O., and Lim, S. N · 2021
Cited alongside, same era.
Fatemi, B., Halcrow, J., and Perozzi, B · 2023
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Towards foundation models for knowledge graph reasoning
Galkin, M., Yuan, X., Mostafa, H., Tang, J., and Zhu, Z · 2023
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Double equivariance for inductive link prediction for both new nodes and new relation types
Gao, J., Zhou, Y., Zhou, J., and Ribeiro, B · 2023
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Grafenne: learning on graphs with heterogeneous and dynamic feature sets
Gupta, S., Manchanda, S., Ranu, S., and Bedathur, S. J · 2023
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Harnessing explanations: Llm-to-lm interpreter for enhanced text-attributed graph representation learning, 2023
He, X., Bresson, X., Laurent, T., Perold, A., LeCun, Y., and Hooi, B · 2023
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Deep graph reprogramming
Jing, Y., Yuan, C., Ju, L., Yang, Y., Wang, X., and Tao, D · 2023
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Multi-task self-supervised graph neural networks enable stronger task generalization
Ju, M., Zhao, T., Wen, Q., Yu, W., Shah, N., Ye, Y., and Zhang, C · 2023
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Graphprompt: Unifying pre-training and downstream tasks for graph neural networks
Liu, Z., Yu, X., Fang, Y., and Zhang, X · 2023
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Highly accurate quantum chemical property prediction with uni-mol+
Lu, S., Gao, Z., He, D., Zhang, L., and Ke, G · 2023
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Luan, S., Hua, C., Xu, M., Lu, Q., Zhu, J., Chang, X.-W., Fu, J., Leskovec, J., and Precup, D · 2023
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McCoy, R. T., Yao, S., Friedman, D., Hardy, M., and Griffiths, T. L · 2023
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Weisfeiler and leman go machine learning: The story so far
Morris, C., Lipman, Y., Maron, H., Rieck, B., Kriege, N. M., Grohe, M., Fey, M., and Borgwardt, K · 2023
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Attending to graph transformers
Müller, L., Galkin, M., Morris, C., and Rampášek, L · 2023
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In-context learning through the bayesian prism
Panwar, M., Ahuja, K., and Goyal, N · 2023
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Why think step-by-step? reasoning emerges from the locality of experience
Prystawski, B. and Goodman, N. D · 2023
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Transferability properties of graph neural networks
Ruiz, L., Chamon, L. F. O., and Ribeiro, A · 2023
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From molecules to materials: Pre-training large generalizable models for atomic property prediction, 2023
Shoghi, N., Kolluru, A., Kitchin, J. R., Ulissi, Z. W., Zitnick, C. L., and Wood, B. M · 2023
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Gpt-4 doesn’t know it’s wrong: An analysis of iterative prompting for reasoning problems
Stechly, K., Marquez, M., and Kambhampati, S · 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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Graphgpt: Graph instruction tuning for large language models
Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., and Huang, C · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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Confidence-based feature imputation for graphs with partially known features
Um, D., Park, J., Park, S., and young Choi, J · 2023
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Digress: Discrete denoising diffusion for graph generation
Vignac, C., Krawczuk, I., Siraudin, A., Wang, B., Cevher, V., and Frossard, P · 2023
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Demystifying oversmoothing in attention-based graph neural networks
Wu, X., Ajorlou, A., Wu, Z., and Jadbabaie, A · 2023
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Mole-BERT: Rethinking pre-training graph neural networks for molecules
Xia, J., Zhao, C., Hu, B., Gao, Z., Tan, C., Liu, Y., Li, S., and Li, S. Z · 2023
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Graph-aware language model pre-training on a large graph corpus can help multiple graph applications
Xie, H., Zheng, D., Ma, J., Zhang, H., Ioannidis, V. N., Song, X., Ping, Q., Wang, S., Yang, C., Xu, Y., et al · 2023
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Vqgraph: Graph vector-quantization for bridging gnns and mlps
Yang, L., Tian, Y., Xu, M., Liu, Z., Hong, S., Qu, W., Zhang, W., Cui, B., Zhang, M., and Leskovec, J · 2023
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Graph domain adaptation via theory-grounded spectral regularization
You, Y., Chen, T., Wang, Z., and Shen, Y · 2023
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Language model beats diffusion–tokenizer is key to visual generation
Yu, L., Lezama, J., Gundavarapu, N. B., Versari, L., Sohn, K., Minnen, D., Cheng, Y., Gupta, A., Gu, X., Hauptmann, A. G., et al · 2023
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Llamarec: Two-stage recommendation using large language models for ranking
Yue, Z., Rabhi, S., Moreira, G. d. S. P., Wang, D., and Oldridge, E · 2023
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Commonscenes: Generating commonsense 3d indoor scenes with scene graphs
Zhai, G., Örnek, E. P., Wu, S.-C., Di, Y., Tombari, F., Navab, N., and Busam, B · 2023
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Graph meets llms: Towards large graph models
Zhang, Z., Li, H., Zhang, Z., Qin, Y., Wang, X., and Zhu, W · 2023
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Accurate structure prediction of biomolecular interactions with alphafold 3
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al · 2024
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Llm2vec: Large language models are secretly powerful text encoders
BehnamGhader, P., Adlakha, V., Mosbach, M., Bahdanau, D., Chapados, N., and Reddy, S · 2024
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Video generation models as world simulators
Brooks, T., Peebles, B., Holmes, C., DePue, W., Guo, Y., Jing, L., Schnurr, D., Taylor, J., Luhman, T., Luhman, E., Ng, C., Wang, R., and Ramesh, A · 2024
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Universal link predictor by in-context learning
Dong, K., Mao, H., Guo, Z., and Chawla, N. V · 2024
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Zero-shot logical query reasoning on any knowledge graph
Galkin, M., Zhou, J., Ribeiro, B., Tang, J., and Zhu, Z · 2024
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G-retriever: Retrieval-augmented generation for textual graph understanding and question answering
He, X., Tian, Y., Sun, Y., Chawla, N. V., Laurent, T., LeCun, Y., Bresson, X., and Hooi, B · 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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Graphinstruct: Empowering large language models with graph understanding and reasoning capability
Luo, Z., Song, X., Huang, H., Lian, J., Zhang, C., Jiang, J., Xie, X., and Jin, H · 2024
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A data generation perspective to the mechanism of in-context learning
Mao, H., Liu, G., Ma, Y., Wang, R., and Tang, J · 2024
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Let your graph do the talking: Encoding structured data for llms
Perozzi, B., Fatemi, B., Zelle, D., Tsitsulin, A., Kazemi, M., Al-Rfou, R., and Halcrow, J · 2024
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Rafi, M. N., Kim, D. J., Chen, A. R., Chen, T.-H., and Wang, S · 2024
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Solving olympiad geometry without human demonstrations
Trinh, T. H., Wu, Y., Le, Q. V., He, H., and Luong, T · 2024
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Exploring the potential of large language models in graph generation
Yao, Y., Wang, X., Zhang, Z., Qin, Y., Zhang, Z., Chu, X., Yang, Y., Zhu, W., and Mei, H · 2024
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Language is all a graph needs
Ye, R., Zhang, C., Wang, R., Xu, S., and Zhang, Y · 2024
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Commonscenes: Generating commonsense 3d indoor scenes with scene graphs
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Beyond weisfeiler-lehman: A quantitative framework for gnn expressiveness
Zhang, B., Gai, J., Du, Y., Ye, Q., He, D., and Wang, L · 2024
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Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning
Zhao, H., Liu, S., Chang, M., Xu, H., Fu, J., Deng, Z., Kong, L., and Liu, Q · 2024
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