Abstract
For current Transformers, although current learnable graph community structures help enhance the information capture of low-degree nodes, these approaches primarily aim to improve the Transformer model rather than deeply exploring the underlying relationships between nodes. As a result, they face limitations in enhancing the role of low-degree nodes within the community. To address these limitations, this paper introduces two innovative methods: Spectral-Mean Synergistic Clustering (SMSC) and the Semantic Proximity Evaluation Mechanism (SPEM). SMSC improves the construction of graph communities, enabling more accurate classification of nodes into different communities. SPEM evaluates the potential value and similarity of low-degree nodes within the same community to establish better connections. The proposed algorithm demonstrates significant performance improvements in node classification. Compared to the baseline model, it achieves a 1.64% improvement on the Cora dataset and a 4.05% improvement on the much larger WikiCS dataset. Experimental results verify that combining spectral-mean co-optimization with semantic proximity assessment significantly enhances the processing of low-degree node information, especially in larger-scale datasets.
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References
Zhao, H., Ma, S., Zhang, D., Deng, Z.H., Wei, F.: Are more layers beneficial to graph transformers? arXiv preprint arXiv:2303.00579 (2023)
Ma, L., Lin, C., Lim, D., et al.: Graph inductive biases in transformers without message passing. In: International Conference on Machine Learning, pp. 23321–23337. PMLR (2023)
Wu, Q., Zhao, W., Yang, C., et al.: Simplifying and empowering transformers for large-graph representations. Adv. Neural Inf. Process. Syst. 36 (2024)
Hoang, V.T., Jeon, H.J., You, E.S., Yoon, Y., Jung, S., Lee, O.J.: Graph representation learning and its applications: a survey. Sensors 23(8), 4168 (2023)
Hoang, V.T., Lee, O., et al.: Mitigating degree biases in message passing mechanism by utilizing community structures. arXiv preprint arXiv:2312.16788 (2023)
Qiu, M., Guo, M., et al.: Loop scheduling and bank type assignment for heterogeneous multi-bank memory. J. Parallel Distrib. Comput. 69(6), 546–558 (2009)
Huang, H., Chaturvedi, V., et al.: Throughput maximization for periodic real-time systems under the maximal temperature constraint. ACM Trans. Embed. Comput. Syst. 13(2s), 1–22 (2014)
Qiu, M., Li, J.: Real-Time Embedded Systems: Optimization, Synthesis, and Networking. CRC Press (2011)
Qiu, M., Dai, W., Vasilakos, A.: Loop parallelism maximization for multimedia data processing in mobile vehicular clouds. IEEE Trans. Cloud Comput. 7(1), 250–258 (2016)
Song, Y., Li, Y., et al.: Retraining strategy-based domain adaption network for intelligent fault diagnosis. IEEE Trans. Industr. Inf. 16(9), 6163–6171 (2019)
Qiu, M., Zhang, K., Huang, M.: Usability in mobile interface browsing. Web Intell. Agent Syst. Intl. J. 4(1), 43–59 (2006)
Wei, X., Guo, H., et al.: Reliable data collection techniques in underwater wireless sensor networks: a survey. IEEE Commun. Surv. Tutorials 24(1), 404–431 (2021)
Qiu, M., Qiu, H.: Review on image processing based adversarial example defenses in computer vision. In: IEEE 6th BigDataSecurity (2020)
Zeng, Y., Qiu, H., et al.: A data augmentation-based defense method against adversarial attacks in neural networks. In: ICA3PP 2020, New York City (2020)
Qiu, H., Zheng, Q., Zhang, T., et al.: Toward secure and efficient deep learning inference in dependable IoT systems. IEEE Internet Things J. 8(5), 3180–3188 (2020)
Zhang, Y., Qiu, M., Gao, H.: Communication-efficient stochastic gradient descent ascent with momentum algorithms. In: IJCAI (2023)
Ling, C., Jiang, J., et al.: Deep graph representation learning and optimization for influence maximization. In: ICML (2023)
Zhang, Y., Qiu, M., et al.: Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst. J. 11(1), 88–95 (2015)
Qiu, M., Gao, W., et al.: Energy efficient security algorithm for power grid wide area monitoring system. IEEE Trans. Smart Grid 2(4), 715–723 (2011)
Zeng, Y., Pan, M., et al.: Narcissus: a practical clean-label backdoor attack with limited information. In: ACM CCS (2023)
Gai, K., Zhang, Y., Qiu, M., Thuraisingham, B.: Blockchain-enabled service optimizations in supply chain digital twin. IEEE Trans. Serv. Comput. (2022)
Li, C., Qiu, M.: Reinforcement Learning for Cyber-Physical Systems: With Cybersecurity Case Studies. CRC Press (2019)
Gai, K., Xu, K., Lu, Z., Qiu, M., Zhu, L.: Fusion of cognitive wireless networks and edge computing. IEEE Wireless Commun. 26(3), 69–75 (2019)
Y. Cui, K. Cao, et al.: Client scheduling and resource management for efficient training in heterogeneous IoT-edge federated learning. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. (2021)
Qiu, H., Qiu, M., Lu, R.: Secure V2X communication network based on intelligent PKI and edge computing. IEEE Netw. 34(2), 172–178 (2019)
Zhang, Y., Yu, X., et al., Every document owns its structure: inductive text classification via graph neural networks. arXiv preprint arXiv:2004.13826 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. Stat 1050(20), 10–48550 (2017)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30 (2017)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)
Lee, N., Hyun, D., Lee, J., Park, C.: Relational self-supervised learning on graphs. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, pp. 1054–1063 (2022)
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 (2020)
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: Proceedings of the Web Conference 2021, pp. 2069–2080 (2021)
Dwivedi, V.P., Bresson, X.: A generalization of transformer networks to graphs. arXiv preprint arXiv:2012.09699 (2020)
Kreuzer, D., Beaini, D., et al.: Rethinking graph transformers with spectral attention. Adv. Neural Inf. Proc. Sys. 34, 21618–21629 (2021)
Chen, D., O’Bray, L., Borgwardt, K.: Structure-aware transformer for graph representation learning. In: International Conference on Machine Learning, pp. 3469–3489. PMLR (2022)
Ikotun, A.M., et al.: K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data. Inf. Sci. 622, 178–210 (2023)
Bianchi, F.M., Grattarola, D., Alippi, C.: Spectral clustering with graph neural networks for graph pooling. In: International Conference on Machine Learning, pp. 874–883. PMLR (2020)
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Li, X., Hu, W., Lu, J., Liu, F., Hu, M., Han, Y. (2024). Performance Enhancement Strategies for Node Classification Based on Graph Community Structure Recognition. In: Cao, C., Chen, H., Zhao, L., Arshad, J., Asyhari, T., Wang, Y. (eds) Knowledge Science, Engineering and Management. KSEM 2024. Lecture Notes in Computer Science(), vol 14884. Springer, Singapore. https://doi.org/10.1007/978-981-97-5492-2_33
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