Influence maximization in social networks with user attitude modification

The aim of influence maximization problem is to find a k-size seed set that has the maximum influence. In previous works the modification of user's attitude is seldom paid attention to. However from the psychology research, we know that people's opinions are affected by their friends. Base on this, we present a new Linear Threshold model with Instant Opinions (LT-IO). We devise an attitude function A
^t
_u
that describes node u's attitude at time t, and the broadcast attitude which is the attitude when a node becomes active. To simulate information propagation in real world, we define a trust threshold η to justify whether a node follows or opposes the influence from its neighbor. We propose a heuristic algorithm IMLT-IOA to solve our problem, prove its submodularity and monotonicity and then obtain its approximation ratio which is (1 - 1/e). To the best of our knowledge, this is the first work that focuses on the influence maximization with user's attitude modification. To verify our IMLT-IOA algorithm, we conduct extensive experiments on a large data collection obtained from real social networks, the results show that IMLT-IOA reduces the running time and meanwhile keeps effectiveness comparing to other algorithms.