兰州大学 李龙杰

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实验室学风优良，定期进行实验室内部的学术讨论和交流，每周召开组会，研究生可随时与导师进行交流、讨论，鼓励学生尽早总结和发表科研成果。欢迎对数据挖掘、复杂网络分析等方向感兴趣的同学加入，也欢迎本科生参与实验室的科研工作。

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主要论文

2024

  1. Yang, W., Li, L.∗, Bai, S., Ma, Z., 2024. IS-GNN: Graph neural network enhanced by aggregating influential and structurally similar nodes. Knowledge-Based Systems 301, 112282. https://doi.org/10.1016/j.knosys.2024.112282
  2. Fang, S., Li, L.∗, Bai, S., Ma, Z.∗, Chen, X. Link Prediction based on Fundamental Heuristic Elements. 2024. https://doi.org/10.1142/S0129183124501614
  3. Mao, C., Li, L.∗, Liu, L., Ma, Z.∗. Identification of Key Classes in Software Systems Based on Static Analysis and Voting Mechanism. International Journal of Software Engineering and Knowledge Engineering, 2024. https://doi.org/10.1142/S0218194024500220
  4. Li, L., Yang, W., Bai, S., Ma, Z., 2024. KNN-GNN: A powerful graph neural network enhanced by aggregating K-nearest neighbors in common subspace. Expert Systems with Applications 253, 124217. https://doi.org/10.1016/j.eswa.2024.124217
  5. Dong, H., Li, L.∗, Tian, D., Sun, Y., Zhao, Y., 2024. Dynamic link prediction by learning the representation of node-pair via graph neural networks. Expert Systems with Applications, 241, 122685. https://doi.org/10.1016/j.eswa.2023.122685
  6. Jia, E., Tian, D., Nan, T., Li, L.∗, 2024. Link Prediction in Dynamic Networks Based on Topological and Historical Information, Theoretical Computer Science, Communications in Computer and Information Science. Springer Nature, pp. 203–220. https://doi.org/10.1007/978-981-99-7743-7_13
  7. Shan, N., Yang, W., Zhang, Z., Li, L.∗., 2024. Link Prediction in Multiplex Network Based on Regression and Conditional Probability, Theoretical Computer Science, Communications in Computer and Information Science. Springer Nature Singapore, pp. 221–236. https://doi.org/10.1007/978-981-99-7743-7_14

2023

  1. Sun, Y., Zhao, Y., Li, L.∗, Dong, H., 2023. Heterogeneous line graph neural network for link prediction. International Conference Advanced Data Mining and Applications (ADMA’23). https://doi.org/10.1007/978-3-031-46677-9_1.
  2. Zhao, Y., Sun, Y., Huang, Y., Li, L.∗, Dong, H., 2023. Link prediction in heterogeneous networks based on metapath projection and aggregation. Expert Systems with Applications, 227, 120325. https://doi.org/10.1016/j.eswa.2023.120325.
  3. Liu, P., Li, L.∗, Wen, Y. & Fang, S. (2023). Identifying influential nodes in social networks: Exploiting self-voting mechanism. Big Data, 11(4), 296-306. https://doi.org/10.1089/big.2022.0165.
  4. 张亚坤, 李龙杰∗, 陈晓云. (2023). 利用朴素贝叶斯模型进行多层网络链接预测. 应用科学学报, 41(1), 23–40. https://doi.org/10.3969/j.issn.0255-8297.2023.01.003.

2022

  1. Luo, H., Li, L.∗, Dong, H., & Chen, X. (2022). Link prediction in multiplex networks: An evidence theory method. Knowledge-Based Systems, 257, 109932. https://doi.org/10.1016/j.knosys.2022.109932
  2. Fang, S., Li, L.∗, Hu, B., & Chen, X. (2022). Evidential link prediction by exploiting the applicability of similarity indexes to nodes. Expert Systems with Applications, 210, 118397. https://doi.org/10.1016/j.eswa.2022.118397
  3. Li, L.∗, Wen, Y., Bai, S. & Liu, P. (2022). Link prediction in weighted networks via motif predictor. Knowledge-Based Systems, 242, 108402. https://doi.org/10.1016/j.knosys.2022.108402
  4. Bai, S., Li, L.∗, & Chen, X. (2022). Conflicting evidence combination based on Belief Mover’s Distance, Journal of Intelligent & Fuzzy Systems, 42(3), 2005-2021. https://doi.org/10.3233/JIFS-211397.

2021

  1. Liu, P., Li, L.∗, Fang, S. & Yao, Y. (2021). Identifying influential nodes in social networks: A voting approach. Chaos, Solitons & Fractals, 152, 111309. https://doi.org/10.1016/j.chaos.2021.111309
  2. Bai, S., Zhang, Y., Li, L.∗, Shan, N., & Chen, X. (2021). Effective Link Prediction in Multiplex Networks: A TOPSIS Method. Expert Systems With Applications, 177, 114973. https://doi.org/10.1016/j.eswa.2021.114973
  3. Li, L.∗, Wang, H., Fang, S., Shan, N., & Chen, X. (2021). A supervised similarity measure for link prediction based on KNN. International Journal of Modern Physics C, 32(9), 2150112. https://doi.org/10.1142/S0129183121501126
  4. Luo, H., Li, L.∗, Fang, S., & Chen, X. (2021). Link Prediction in Multiplex Networks using a Novel Multiple-Attribute Decision-Making Approach. Knowledge-Based Systems, 219, 106904. https://doi.org/10.1016/j.knosys.2021.106904
  5. Li, L.∗, Wang, L., Luo, H., & Chen, X. (2021). Towards effective link prediction: A hybrid similarity model. Journal of Intelligent & Fuzzy Systems, 40(3). https://doi.org/10.3233/JIFS-200344

2020

  1. Shan, N., Li, L.∗, Zhang, Y., Bai, S., & Chen, X. (2020). Supervised link prediction in multiplex networks. Knowledge-Based Systems, 203, 106168. https://doi.org/10.1016/j.knosys.2020.106168
  2. 李龙杰∗, 胡江龙, 陈晓云. (2020). 基于节点连接模式的缺失节点识别方法. 华中科技大学学报（自然科学版）, 48(8), 91–97. https://doi.org/10.13245/j.hust.200816
  3. 刘昱阳, 李龙杰∗, 单娜, 陈晓云. (2020). 融合聚集系数的链接预测方法. 计算机应用, 40(1), 28–35. https://doi.org/10.11772/j.issn.1001-9081.2019061008

2019

  1. Bai, S., Fang, S., Li, L.∗, Liu, R., & Chen, X. (2019). Enhancing link prediction by exploring community membership of nodes. International Journal of Modern Physics B, 33(31), 1950382. https://doi.org/10.1142/S021797921950382X
  2. Li, L.∗, Wang, L., Bai, S., Fang, S., Cheng, J., & Chen, X. (2019). An effective similarity measure based on kernel spectral method for complex networks. International Journal of Modern Physics C, 30(7), 1–21. https://doi.org/10.1142/S0129183119400059
  3. Li, L.∗, Xu, S., Leng, M., Fang, S., & Chen, X. (2019). Predicting top-L missing links: An improved Local Naïve Bayes model. IEEE Access, 7, 1–1. https://doi.org/10.1109/ACCESS.2019.2914724
  4. Li, L.∗, Fang, S., Bai, S., Xu, S., Cheng, J., & Chen, X. (2019). Effective Link Prediction Based on Community Relationship Strength. IEEE Access, 7, 43233–43248. https://doi.org/10.1109/ACCESS.2019.2908208
  5. Cheng, J., Su, X., Yang, H., Li, L., Zhang, J., Zhao, S., & Chen, X. (2019). Neighbor Similarity Based Agglomerative Method for Community Detection in Networks. Complexity, 2019, 1–16. https://doi.org/10.1155/2019/8292485
  6. 单娜, 李龙杰∗, 刘昱阳, 陈晓云. (2019). 基于节点连接模式相关性的链接预测方法. 计算机科学, 46(12), 20. https://doi.org/10.11896/jsjkx.190700057

2018

  1. Li, L.∗, Bai, S., Leng, M., Wang, L., & Chen, X. (2018). Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method. Complexity, 2018, 1–16. https://doi.org/10.1155/2018/3579758
  2. Bai, S., Li, L.∗, Cheng, J., Xu, S., & Chen, X. (2018). Predicting Missing Links Based on a New Triangle Structure. Complexity, 2018, 1–11. https://doi.org/10.1155/2018/7312603
  3. Li, L.∗, Yu, Y., Bai, S., Hou, Y., & Chen, X. (2018). An Effective Two-Step Intrusion Detection Approach Based on Binary Classification and k-NN. IEEE Access, 6, 12060–12073. https://doi.org/10.1109/ACCESS.2017.2787719
  4. Li, L.∗, Yu, Y., Bai, S., Cheng, J., & Chen, X. (2018). Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO. Journal of Sensors, 2018, 1–9. https://doi.org/10.1155/2018/1578314
  5. Cheng, J., Yin, X., Li, Q., Yang, H., Li, L., Leng, M., & Chen, X. (2018). Voting Simulation based Agglomerative Hierarchical Method for Network Community Detection. Scientific Reports, 8(1), 8064. https://doi.org/10.1038/s41598-018-26415-3

2017 and before

  1. Pan, L., Feng, X., Sang, F., Li, L., Leng, M., & Chen, X. (2017). An improved back propagation neural network based on complexity decomposition technology and modified flower pollination optimization for short-term load forecasting. Neural Computing and Applications, 31(7), 2679–2697. https://doi.org/10.1007/s00521-017-3222-2
  2. Chen, M., Li, L., Wang, B., Cheng, J., Pan, L., & Chen, X. (2016). Effectively clustering by finding density backbone based-on k NN. Pattern Recognition, 60, 486–498. https://doi.org/10.1016/j.patcog.2016.04.018
  3. Cheng, J., Li, L., Leng, M., Lu, W., Yao, Y., & Chen, X. (2016). A divisive spectral method for network community detection. Journal of Statistical Mechanics: Theory and Experiment, 2016(3), 033403. https://doi.org/10.1088/1742-5468/2016/03/033403
  4. Li, L.∗, Qian, L., Wang, X., Luo, S., & Chen, X. (2015). Accurate similarity index based on activity and connectivity of node for link prediction. International Journal of Modern Physics B, 29(17). https://doi.org/10.1142/S0217979215501088
  5. Li, L.∗, Qian, L., Cheng, J., Ma, M., & Chen, X. (2015). Accurate similarity index based on the contributions of paths and end nodes for link prediction. Journal of Information Science, 41(2), 167–177. https://doi.org/10.1177/0165551514560121
  6. Yao, Y., Cui, H., Liu, Y., Li, L., Zhang, L., & Chen, X. (2015). PMSVM: An optimized support vector machine classification algorithm based on pca and multilevel grid search methods. Mathematical Problems in Engineering, 2015. https://doi.org/10.1155/2015/320186
  7. Leng, M., Huang, L., Li, L., Zhou, H., Cheng, J., & Chen, X. (2015). Active semisupervised community detection based on asymmetric similarity measure. International Journal of Modern Physics B, 29(13). https://doi.org/10.1142/S0217979215500782
  8. Jin, R., Lee, V. E., & Li, L. (2014). Scalable and axiomatic ranking of network role similarity. ACM Transactions on Knowledge Discovery from Data, 8(1). https://doi.org/10.1145/2518176
  9. Li, L., Ma, M., Lei, P., Leng, M., & Chen, X. (2014). S2R&R2S: A framework for ranking vertex and computing vertex-pair similarity simultaneously. Journal of Information Science, 40(6). https://doi.org/10.1177/0165551514542902
  10. Li, L., Ma, M., Lei, P., Wang, X., & Chen, X. (2014). A linear approximate algorithm for earth mover’s distance with thresholded ground distance. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/406358