Identifying Top-k Most Influential Nodes by using the Topological Diffusion Models in the Complex Networks

Social networks are sub-set of complex networks, where users are defined as nodes, and the connections between users are edges. One of the important issues concerning social network analysis is identifying influential and penetrable nodes. Centrality is an important method among many others practiced for identification of influential nodes. Centrality criteria include degree centrality, betweenness centrality, closeness centrality, and Eigenvector centrality; all of which are used in identifying those influential nodes in weighted and weightless networks. TOPSIS is another basic and multi-criteria method which employs four criteria of centrality simultaneously to identify influential nodes; a fact that makes it more accurate than the above criteria. Another method used for identifying influential or top-k influential nodes in complex social networks is Heat Diffusion Kernel: As one of the Topological Diffusion Models; this model identifies nodes based on heat diffusion. In the present paper, to use the topological diffusion model, the social network graph is drawn up by the interactive and non-interactive activities; then, based on the diffusion, the dynamic equations of the graph are modeled. This was followed by using improved heat diffusion kernels to improve the accuracy of influential nodes identification. After several re-administrations of the topological diffusion models, those users who diffused more heat were chosen as the most influential nodes in the concerned social network. Finally, to evaluate the model, the current method was compared with Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS).

@article{Paidar2017,
  title     = {Identifying Top-k Most Influential Nodes by using the Topological Diffusion Models in the Complex Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {6},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Maryam Paidar and Sarkhosh Seddighi Chaharborj and Ali Harounabadi},
  doi       = {10.14569/IJACSA.2017.080638},
  url       = {https://doi.org/10.14569/IJACSA.2017.080638}
}