Anchored Self-Supervised Dynamic Graph Representation Learning for Aviation Data as A Fast Economic Indicator
How to cite (IJASEIT) :
M. Belitski, C. Guenther, A. S. Kritikos, and R. Thurik, “Economic effects of the COVID-19 pandemic on entrepreneurship and small businesses,” Small Business Economics, vol. 58, no. 2, pp. 593–609, Feb. 2022, doi: 10.1007/s11187-021-00544-y.
A. Haldane and S. Chowla, “Fast economic indicators,” Nature Reviews Physics, vol. 3, no. 2. Springer Nature, pp. 68–69, Feb. 01, 2021. doi: 10.1038/s42254-020-0236-y.
P. Aguilar, C. Ghirelli, M. Pacce, and A. Urtasun, “Can news help measure economic sentiment? An application in COVID-19 times,” Econ Lett, vol. 199, p. 109730, Feb. 2021, doi:10.1016/j.econlet.2021.109730.
G. Aoki, K. Ataka, T. Doi, and K. Tsubouchi, “Data-driven estimation of economic indicators with search big data in discontinuous situation,” The Journal of Finance and Data Science, vol. 9, p. 100106, Nov. 2023, doi: 10.1016/j.jfds.2023.100106.
S. Lehrer, T. Xie, and X. Zhang, “Social media sentiment, model uncertainty, and volatility forecasting,” Econ Model, vol. 102, p. 105556, Sep. 2021, doi: 10.1016/j.econmod.2021.105556.
IATA, “IATA 2022 Air Connectivity Report,” 2022.
Y. Zhang, Y. Xiong, D. Li, C. Shan, K. Ren, and Y. Zhu, “CoPE: Modeling Continuous Propagation and Evolution on Interaction Graph,” in International Conference on Information and Knowledge Management, Proceedings, Association for Computing Machinery, Oct. 2021, pp. 2627–2636. doi: 10.1145/3459637.3482419.
F. Aziz, L. T. Slater, L. Bravo-Merodio, A. Acharjee, and G. V. Gkoutos, “Link prediction in complex network using information flow,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-41476-9.
P. Mei and Y. hong Zhao, “Dynamic network link prediction with node representation learning from graph convolutional networks,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-023-50977-6.
M. Zou, Z. Gan, R. Cao, C. Guan, and S. Leng, “Similarity-navigated graph neural networks for node classification,” Inf Sci (N Y), vol. 633, pp. 41–69, Jul. 2023, doi: 10.1016/j.ins.2023.03.057.
Y. Zhu, J. Wang, J. Zhang, and K. Zhang, “Node Embedding and Classification with Adaptive Structural Fingerprint,” Neurocomputing, vol. 502, pp. 196–208, Sep. 2022, doi:10.1016/j.neucom.2022.05.073.
R. Bhattacharya, N. K. Nagwani, and S. Tripathi, “A community detection model using node embedding approach and graph convolutional network with clustering technique,” Decision Analytics Journal, vol. 9, p. 100362, Dec. 2023, doi:10.1016/j.dajour.2023.100362.
A. Celikkanat, F. D. Malliaros, and A. N. Papadopoulos, “NodeSig: Binary Node Embeddings via Random Walk Diffusion,” in Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 68–75. doi: 10.1109/asonam55673.2022.10068621.
S. N. Mohammed and S. Gündüç, “Degree-based random walk approach for graph embedding,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 30, no. 5, pp. 1868–1881, 2022, doi: 10.55730/1300-0632.3910.
A. Tomčić, M. Savić, and M. Radovanović, “Hub‐aware random walk graph embedding methods for classification,” Stat. Anal. Data Min., vol. 17, no. 2, Apr. 2024, doi: 10.1002/sam.11676.
H. Zhang, G. Kou, Y. Peng, and B. Zhang, “Role-aware random walk for network embedding,” Inf Sci (N Y), vol. 652, p. 119765, Jan. 2024, doi: 10.1016/j.ins.2023.119765.
T. N. Kipf and M. Welling, “Semi-Supervised Classification with Graph Convolutional Networks,” in International Conference on Learning Representations, 2017. [Online]. Available: https://openreview.net/forum?id=SJU4ayYgl
W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, in NIPS’17. Red Hook, NY, USA: Curran Associates Inc., 2017, pp. 1025–1035.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph Attention Networks,” International Conference on Learning Representations, 2018, [Online]. Available: https://openreview.net/forum?id=rJXMpikCZ.
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How Powerful are Graph Neural Networks?” in International Conference on Learning Representations, 2019. [Online]. Available: https://openreview.net/forum?id=ryGs6iA5Km.
W. W. Lo, S. Layeghy, M. Sarhan, M. Gallagher, and M. Portmann, “E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT,” in NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, IEEE Press, 2022, pp. 1–9. doi:10.1109/noms54207.2022.9789878.
K. Yang, Y. Liu, Z. Zhao, X. Zhou, and P. Ding, “Graph attention network via node similarity for link prediction,” The European Physical Journal B: Condensed Matter and Complex Systems, vol. 96, no. 3, pp. 1–10, Mar. 2023, doi: 10.1140/epjb/s10051-023-0.
S. M. Kazemi et al., “Representation Learning for Dynamic Graphs: A Survey,” 2020. [Online]. Available: http://jmlr.org/papers/v21/19-447.html.
X. Huang, J. Li, and Y. Yuan, “Link Prediction in Dynamic Social Networks Combining Entropy, Causality, and a Graph Convolutional Network Model,” Entropy, vol. 26, no. 6, 2024, doi:10.3390/e26060477.
X. Mo, J. Pang, and Z. Liu, “Deep autoencoder architecture with outliers for temporal attributed network embedding,” Expert Syst Appl, vol. 240, p. 122596, Apr. 2024, doi: 10.1016/j.eswa.2023.122596.
L. Liu, H. Zhao, and Z. Hu, “Graph dynamic autoencoder for fault detection,” Chem Eng Sci, vol. 254, p. 117637, Jun. 2022, doi: 10.1016/J.CES.2022.117637.
E. Yu, Y. Fu, J. Zhou, H. Sun, and D. Chen, “Predicting Critical Nodes in Temporal Networks by Dynamic Graph Convolutional Networks,” Applied Sciences (Switzerland), vol. 13, no. 12, Jun. 2023, doi:10.3390/app13127272.
P. Zhang et al., “Continual Learning on Dynamic Graphs via Parameter Isolation,” in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, in SIGIR ’23. New York, NY, USA: Association for Computing Machinery, 2023, pp. 601–611. doi:10.1145/3539618.3591652.
J. Liu, C. Xu, C. Yin, W. Wu, and Y. Song, “K-Core Based Temporal Graph Convolutional Network for Dynamic Graphs,” IEEE Trans Knowl Data Eng, vol. 34, no. 8, pp. 3841–3853, 2022, doi:10.1109/tkde.2020.3033829.
A. Sankar, Y. Wu, L. Gou, W. Zhang, and H. Yang, “DYSAT: Deep neural representation learning on dynamic graphs via self-attention networks,” in WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining, Association for Computing Machinery, Inc, Jan. 2020, pp. 519–527. doi:10.1145/3336191.3371845.
R. Bhattacharya, N. Nagwani, and S. Tripathi, “Detecting influential nodes with topological structure via Graph Neural Network approach in social networks,” International Journal of Information Technology, vol. 15, Jul. 2023, doi: 10.1007/s41870-023-01271-1.
M. R. F. Mendonça, A. M. S. Barreto, and A. Ziviani, “Approximating Network Centrality Measures Using Node Embedding and Machine Learning,” IEEE Trans Netw Sci Eng, vol. 8, no. 1, pp. 220–230, 2021, doi: 10.1109/tnse.2020.3035352.
S. Kumar, A. Mallik, A. Khetarpal, and B. S. Panda, “Influence maximization in social networks using graph embedding and graph neural network,” Inf Sci (N Y), vol. 607, pp. 1617–1636, Aug. 2022, doi: 10.1016/j.ins.2022.06.075.
X. H. Yang et al., “Identifying influential spreaders in complex networks based on network embedding and node local centrality,” Physica A: Statistical Mechanics and its Applications, vol. 573, p. 125971, Jul. 2021, doi: 10.1016/j.physa.2021.125971.
Q. Hu, J. Jiang, H. Xu, and M. Kassim, “IMNE: Maximizing influence through deep learning-based node embedding in social network,” Swarm Evol Comput, vol. 88, p. 101609, Jul. 2024, doi:10.1016/j.swevo.2024.101609.
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