Identifying emerging influential Nodes in evolving networks: Exploiting strength of weak nodes
This work addresses the need for more accurate prediction of future influential nodes in dynamic networks, though it appears incremental as it builds on existing methods by integrating two previously separate approaches.
The paper tackles the problem of identifying emerging influential nodes in evolving networks by proposing a hybrid model that combines structural centrality with recent node activity, showing better performance than baseline methods on three real datasets.
Identifying emerging influential or popular node/item in future on network is a current interest of the researchers. Most of previous works focus on identifying leaders in time evolving networks on the basis of network structure or node's activity separate way. In this paper, we have proposed a hybrid model which considers both, node's structural centrality and recent activity of nodes together. We consider that the node is active when it is receiving more links in a given recent time window, rather than in the whole past life of the node. Furthermore our model is flexible to implement structural rank such as PageRank and webpage click information as activity of the node. For testing the performance of our model, we adopt the PageRank algorithm and linear preferential attachment based model as the baseline methods. Experiments on three real data sets (i.e Movielens, Netflix and Facebook wall post data set), we found that our model shows better performance in terms of finding the emerging influential nodes that were not popular in past.