• Klotilda Nikaj University of Tirana, Albania
  • Margarita Ifti University of Tirana, Albania


information diffusion, influential nodes, competitive models


The dynamic of the information distribution is complex and affects the everyday life, especially with the multiple ideas that are being released in social networks. These multiple ideas often have competing nature for the same nodes of the network. In this paper we will discuss two competitive influence diffusion models in social networks. First, we take in consideration the competitive cascade model and at a certain point, the network will behave according to a competitive linear threshold dynamic model. We will assume that when a certain number of the nodes will have a specific information and when this number will grow up beyond a critical percentage of the active nodes, then we will reach an automatic collective change behavior of the network. Each node in the network can be inactive or active (positively or negatively). If we can control the diffusion processes of the information in the network, we can predict the popularity of the innovation introduced. Since is a challenging task to predict the popularity of an innovation, by analyzing the dynamic of the network according to the competitive diffusion models as extended of single item diffusion. By joining the two models for the diffusion of the information on the directed networks, we will better understand the dynamic of the information distribution through the network, and how this distribution is affected from the different attitudes of the individuals when they firstly have this information. By combining and analyzing the two competitive diffusion models, extending them with two influence distribution functions, we take as a result as a better way to outperform in term of efficiency and effectiveness


Arora, A., Galhotra, S., & Ranu, S. (2017).‘‘Debunking the myths of influence maximization: An in-depth benchmarking study,’’ in Proc. SIGMOD Conf, pp. 651–666.

Clifford, P., & Sudbury, A.(1973).‘‘A model for spatial conflict,’’ Biometrika, vol. 60, no. 3, pp. 581–588.

Hu, W., Cao G., Krishanamurthy S. V., & Mohapatra, P. (2013).“Mobilityassisted energy-aware user contact detection in mobile social networks,” in Proc. of IEEE ICDCS.

Jiang, Q., Song, G., Cong, G., Wang, Y., Si, W., & Xie, K. (2011). “Simulated annealing based influence maximization in social networks,” in Proc. of AAAI.

Liben-Nowell, D., & Kleinberg, J. (2008).“Tracing information flow on a global scale using internet chain-letter data,” Proceedings of the National Academy of Sciences, vol. 105, no. 12, pp. 4633–4638.

Lancichinetti, A., & Fortunato, S. (2009).“Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities,” Physical Review E, vol. 80, no. 1, p. 016118.

Litou, J., Kalogeraki, V., & Gunopulos, D. (2017).‘‘Influence maximization in a many cascades world,’’ in Proc. IEEE 37th Int. Conf. Distrib. Comput. Syst. (ICDCS), pp. 911–921.

Li, Y., Chen, W., Wang, Y., & Zhang, Z.-L. (2013).‘‘Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships,’’ in Proc. WSDM, pp. 657–666.

Ning, T., Yang Z., Wu H., & Han Z., (2013).“Self-interest-drive incentives for ad dissemination in autonomous mobile social networks,” in Proc. of IEEE INFOCOM.

Newman, M. E. (2005).“A measure of betweenness centrality based on random walks,” Social networks, vol. 27, no. 1, pp. 39–54.

Lu, Z., Wen, Y., & Cao, G. (2013).“Community detection in weighted networks: Algorithms and applications,” in Proc. of IEEE PerCom.

Srivastava, A., Chelmis, C., & Prasanna, V. K. (2016).‘‘Computing competing cascades on signed networks,’’ Social Netw. Anal. Mining, vol. 6, p. 82.

Tong, G., Wu, W., Tang, S., & Du, D.( 2017) ‘‘Adaptive influence maximization in dynamic social networks,’’ IEEE/ACM Trans. Netw., vol. 25, no. 1, pp. 112–125.

Wang, Y., Fan, Q., Li, Y., & Tan, K.-L. (2017).‘‘Real-time influence maximization on dynamic social streams,’’ Proc. VLDB Endowment, vol. 10, no. 7, pp. 805–816.




How to Cite

Nikaj, K., & Ifti, M. (2022). COMPETITIVE INFLUENCE DIFFUSION THROUGH SOCIAL NETWORKS. KNOWLEDGE - International Journal , 51(3), 487–491. Retrieved from