  Understanding the behaviors of information propagation is essential for the
effective exploitation of social influence in social networks. However, few
existing influence models are both tractable and efficient for describing the
information propagation process and quantitatively measuring social influence.
To this end, in this paper, we develop a linear social influence model, named
Circuit due to its close relation to the circuit network. Based on the
predefined four axioms of social influence, we first demonstrate that our model
can efficiently measure the influence strength between any pair of nodes. Along
this line, an upper bound of the node(s)' influence is identified for potential
use, e.g., reducing the search space. Furthermore, we provide the physical
implication of the Circuit model and also a deep analysis of its relationships
with the existing methods, such as PageRank. Then, we propose that the Circuit
model provides a natural solution to the problems of computing each single
node's authority and finding a set of nodes for social influence maximization.
At last, the effectiveness of the proposed model is evaluated on the real-world
data. The extensive experimental results demonstrate that Circuit model
consistently outperforms the state-of-the-art methods and can greatly alleviate
the computation burden of the influence maximization problem.
