[1] Xu G.Q., Miao J.L. and Dong C.* (2025). LGP-DS: A novel algorithm for identifying influential nodes in complex networks based on multi-dimensional evidence fusion. EPL, Early Access.

[2] Pan X.H., Xu G.Q., and Dong C. (2025). Link prediction in complex networks based on resource transition capacity and local paths. Modern Physics Letters B, Accept.

[3] Meng L., Xu G.Q., Dong C.and Wang S.J.(2025). Modeling information propagation for target user groups in online social networks based on guidance and incentive strategies. Information Sciences, 691: 121628.

[4] Meng L., Xu G.Q. and Dong C. (2025). An improved gravity model for identifying influential nodes in complex networks considering asymmetric attraction effect. Physica A-Statistical Mechanics and Its Applications, 657, 130237.

[5] Dong C.*, Wang H.C., Zhou S.Y. and Zhong H.L. (2024). SEIDR: modeling the competitive propagation of rumor and anti-rumor in complex networks with emotional infection theory. European Physical Journal Plus, 2024, 139: 987.

[6] Xu G.Q., Dong C.* (2024). CAGM: A communicability-based adaptive gravity model for influential nodes identification in complex networks. Expert Systems with Applications, 235, 121154.

[7] Dong C., Xu G.Q. and Meng L. (2024). CRB: A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization. Chinese Physics B, 33 (8): 088901.

[8] Dong C., Xu G.Q.*, Yang P.L. and Meng L. (2023). TSIFIM: A three-stage iterative framework for influence maximization in complex networks. Expert Systems with Applications,212, 118702.

[9] Yang P.L., Zhao L.J., Dong C., Xu G.Q. and Zhou L.X. (2023). AIGCrank: A new adaptive algorithm for identifying a set of influential spreaders in complex networks based on gravity centrality. Chinese Physics B, 32 (5): 058901.

[10] Dong C., Xu G.Q.*, Meng L. and Yang P.L. (2022). CPR-TOPSIS: A novel algorithm for finding influential nodes in complex networks based on communication probability and relative entropy. Physica A-Statistical Mechanics and Its Applications, 603, 127797.

[11] Xu G.Q., Dong C. and Meng L.* (2022). Research on the Collaborative Innovation Relationship of Artificial Intelligence Technology in Yangtze River Delta of China: A Complex Network Perspective. Sustainability, 14, 14002.

[12] Lu P.L.*, Dong C. and Guo Y.H.(2022).A novel method based on node’s correlation to evaluate the important nodes in complex networks. Journal of Shanghai Jiao Tong University (Science), 27, 688–698.

[13] Peng J., Xu G.Q., Zhou X.Y., Dong C. and Meng L. (2022). Link prediction in complex networks based on communication capacity and local paths. European Physical Journal B, 95(9), 152.

[14] Xu G.Q., Zhou X.Y., Peng J. and Dong C. (2022). SCL-WTNS: A new link prediction algorithm based on strength of community link and weighted two-level neighborhood similarity. International Journal of Modern Physics B, 36(20), 2250120.

[15] Lu P.L.*, Dong C. (2020). EMH: Extended Mixing H-index centrality for identification important users in social networks based on neighborhood diversity. Modern Physics Letters B, 34(26), 2050284.

[16] Lu P.L.*, Dong C. (2020). Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure. International Journal of Modern Physics B, 33(32), 1950395.