Fetching the paper…
Reading the bibliography…
Graph neural networks (GNNs) have shown advantages in graph-based analysis tasks.
S. Kullback and R. A. Leibler, “On information and sufficiency,” The annals of mathematical statistics , vol. 22, no. 1, pp. 79–86, 1951
1951
Earlier work this paper cites.
T. M. Cover, J. A. Thomas et al. , “Entropy, relative entropy and mutual information,” Elements of information theory , vol. 2, no. 1, pp. 12–13, 1991
1991
Earlier work this paper cites.
J. Lin, “Divergence measures based on the shannon entropy,” IEEE Transactions on Information theory , vol. 37, no. 1, pp. 145–151, 1991
1991
Earlier work this paper cites.
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, “Collective classification in network data,” AI magazine , vol. 29, no. 3, pp. 93–93, 2008
2008
Earlier work this paper cites.
G. Namata, B. London, L. Getoor, B. Huang, and U. Edu, “Query-driven active surveying for collective classification,” in 10th International Workshop on Mining and Learning with Graphs , vol. 8, 2012, p. 1
2012
Earlier work this paper cites.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” 2016
2016
Earlier work this paper cites.
A. P. García-Plaza, V. Fresno-Fernández, R. Martínez-Unanue, and A. Zubiaga, “Using fuzzy logic to leverage HTML markup for web page representation,” IEEE Trans. Fuzzy Syst. , vol. 25, no. 4, pp. 919–933, 2017
2017
Earlier work this paper cites.
W. L. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in NeurIPS , vol. 30, 2017, pp. 1024–1034
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR , 2017, pp. 1–14
2017
Earlier work this paper cites.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” NeurIPS , 2017
2017
Earlier work this paper cites.
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” 2017
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in ICLR , 2018, pp. 1–12
2018
Earlier work this paper cites.
Q. Zhang, M. Li, and Y. Deng, “Measure the structure similarity of nodes in complex networks based on relative entropy,” Physica A: Statistical Mechanics and its Applications , vol. 491, pp. 749–763, 2018
2018
Earlier work this paper cites.
S. Abu-El-Haija, B. Perozzi, A. Kapoor, N. Alipourfard, K. Lerman, H. Harutyunyan, G. Ver Steeg, and A. Galstyan, “Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing,” in ICML , 2019, pp. 21–29
2019
Earlier work this paper cites.
M. Fey and J. E. Lenssen, “Fast graph representation learning with PyTorch Geometric,” in ICLR Workshop on Representation Learning on Graphs and Manifolds , 2019, pp. 1–12
2019
Earlier work this paper cites.
Z. Jia, J. Thomas, T. Warszawski, M. Gao, M. Zaharia, and A. Aiken, “Optimizing DNN computation with relaxed graph substitutions,” in Proceedings of Machine Learning and Systems , 2019
2019
Earlier work this paper cites.
M. Wang, D. Zheng, Z. Ye, Q. Gan, M. Li, X. Song, J. Zhou, C. Ma, L. Yu, Y. Gai et al. , “Deep graph library: A graph-centric, highly-performant package for graph neural networks,” 2019
2019
Earlier work this paper cites.
J. Zhao, Z. Zhou, Z. Guan, W. Zhao, W. Ning, G. Qiu, and X. He, “Intentgc: A scalable graph convolution framework fusing heterogeneous information for recommendation,” in SIGKDD , 2019, pp. 2347–2357
2019
Earlier work this paper cites.
J. Fang, Y. Shen, Y. Wang, and L. Chen, “Optimizing DNN computation graph using graph substitutions,” Proc. VLDB Endow. , vol. 13, no. 11, pp. 2734–2746, 2020
2020
Earlier work this paper cites.
Y. M. Omar and P. Plapper, “A survey of information entropy metrics for complex networks,” Entropy , vol. 22, no. 12, p. 1417, 2020
2020
Cited alongside, same era.
H. Pei, B. Wei, K. C. Chang, Y. Lei, and B. Yang, “Geom-gcn: Geometric graph convolutional networks,” in ICLR , 2020, pp. 1–12
2020
Cited alongside, same era.
J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun, “Graph neural networks: A review of methods and applications,” AI Open , vol. 1, pp. 57–81, 2020
2020
Cited alongside, same era.
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra, “Beyond homophily in graph neural networks: Current limitations and effective designs,” in NeurIPS , vol. 33, 2020, pp. 7793–7804
2020
Cited alongside, same era.
Y. Han, G. Li, H. Yuan, and J. Sun, “An autonomous materialized view management system with deep reinforcement learning,” in ICDE , 2021, pp. 2159–2164
Y. Yan, M. Hashemi, K. Swersky, Y. Yang, and D. Koutra, “Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks,” in ICDM , 2022, pp. 1287–1292
2022
Later among the works it cites.
T. Yang, Y. Wang, Z. Yue, Y. Yang, Y. Tong, and J. Bai, “Graph pointer neural networks,” in AAAI , 2022, pp. 8832–8839
2022
Later among the works it cites.
H. Yuan, G. Li, and Z. Bao, “Route travel time estimation on A road network revisited: Heterogeneity, proximity, periodicity and dynamicity,” Proc. VLDB Endow. , vol. 16, no. 3, pp. 393–405, 2022
2022
Later among the works it cites.
X. Zheng, Y. Liu, S. Pan, M. Zhang, D. Jin, and P. S. Yu, “Graph neural networks for graphs with heterophily: A survey,” 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
D. Jin, Z. Yu, C. Huo, R. Wang, X. Wang, D. He, and J. Han, “Universal graph convolutional networks,” in NeurIPS , vol. 34, 2021, pp. 10 654–10 664
2021
Cited alongside, same era.
W. Jin, T. Derr, Y. Wang, Y. Ma, Z. Liu, and J. Tang, “Node similarity preserving graph convolutional networks,” in WSDM , 2021, pp. 148–156
2021
Cited alongside, same era.
M. Liu, Z. Wang, and S. Ji, “Non-local graph neural networks,” 2021
2021
Cited alongside, same era.
G. Luo, J. Li, H. Peng, C. Yang, L. Sun, P. S. Yu, and L. He, “Graph entropy guided node embedding dimension selection for graph neural networks,” IJCAI , pp. 2767–2774, 2021
2021
Cited alongside, same era.
A. Raffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann, “Stable-baselines3: Reliable reinforcement learning implementations,” JMLR , vol. 22, no. 268, pp. 1–8, 2021
2021
Cited alongside, same era.
B. Rozemberczki, C. Allen, and R. Sarkar, “Multi-scale attributed node embedding,” Journal of Complex Networks , vol. 9, no. 2, p. cnab014, 2021
2021
Cited alongside, same era.
H. Yuan and G. Li, “A survey of traffic prediction: from spatio-temporal data to intelligent transportation,” Data Sci. Eng. , vol. 6, no. 1, pp. 63–85, 2021
2021
Cited alongside, same era.
2023
Closest in time.
Z. Gu, K. Zhang, G. Bai, L. Chen, L. Zhao, and C. Yang, “Dynamic activation of clients and parameters for federated learning over heterogeneous graphs,” in ICDE , 2023, pp. 1597–1610
2023
Closest in time.
S. Horchidan, “Query optimization for inference-based graph databases,” in VLDB , ser. CEUR Workshop Proceedings, V. Efthymiou and X. Hu, Eds., vol. 3452, 2023, pp. 33–36
2023
Closest in time.
C. Li, Y. Tsai, and J. C. Liao, “Graph neural networks for tabular data learning,” in ICDE , 2023, pp. 3589–3592
2023
Closest in time.
Y. Li, H. Yuan, Z. Fu, X. Ma, M. Xu, and S. Wang, “ELASTIC: edge workload forecasting based on collaborative cloud-edge deep learning,” in WWW , Y. Ding, J. Tang, J. F. Sequeda, L. Aroyo, C. Castillo, and G. Houben, Eds., 2023, pp. 3056–3066
2023
Closest in time.
Y. Li, H. Xiong, L. Kong, S. Wang, Z. Sun, H. Chen, G. Chen, and D. Yin, “Ltrgcn: Large-scale graph convolutional networks-based learning to rank for web search,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases , 2023, pp. 635–651
2023
Closest in time.
Y. Li, H. Xiong, L. Kong, R. Zhang, F. Xu, G. Chen, and M. Li, “Mhrr: Moocs recommender service with meta hierarchical reinforced ranking,” IEEE Transactions on Services Computing , 2023
2023
Closest in time.
T. Peng, Y. Liang, W. Wu, J. Ren, Z. Pengrui, and Y. Pu, “Clgt: A graph transformer for student performance prediction in collaborative learning,” in AAAI , vol. 37, no. 13, 2023, pp. 15 947–15 954
2023
Closest in time.
Z. Shen, C. Hu, and Z. Zhao, “Lynx: A graph query framework for multiple heterogeneous data sources,” Proc. VLDB Endow. , vol. 16, no. 12, pp. 3926–3929, 2023
2023
Closest in time.
C. Wu, C. Wang, J. Xu, Z. Fang, T. Gu, C. Wang, Y. Song, K. Zheng, X. Wang, and G. Zhou, “Instant representation learning for recommendation over large dynamic graphs,” in ICDE , 2023, pp. 82–95
2023
Closest in time.
Y. Yang, Z. Guan, J. Li, W. Zhao, J. Cui, and Q. Wang, “Interpretable and efficient heterogeneous graph convolutional network,” TKDE , vol. 35, no. 2, pp. 1637–1650, 2023
2023
Closest in time.
2023
Closest in time.
Y. Zhang, Z. Zhou, Q. Yao, X. Chu, and B. Han, “Adaprop: Learning adaptive propagation for graph neural network based knowledge graph reasoning,” in SIGKDD . ACM, 2023, pp. 3446–3457
2023
Closest in time.
Y. Zhang, W. Wang, H. Yin, P. Zhao, W. Chen, and L. Zhao, “Disconnected emerging knowledge graph oriented inductive link prediction,” in ICDE , 2023, pp. 381–393
2023
Closest in time.
S. Zheng, W. Wang, J. Qu, H. Yin, W. Chen, and L. Zhao, “MMKGR: multi-hop multi-modal knowledge graph reasoning,” in ICDE , 2023, pp. 96–109
2023
Closest in time.
G. Zhu, Z. Zhu, W. Wang, Z. Xu, C. Yuan, and Y. Huang, “Autoac: Towards automated attribute completion for heterogeneous graph neural network,” in ICDE , 2023, pp. 2808–2821
2023
Closest in time.