TsFSIM: a three-step fast selection algorithm for influence maximisation in social network - Liqing Qiu; Shiqi Sai; Xiangbo Tian (Connection Science)
Influence maximisation is the problem of selecting a specific number of nodes which can maximise the influence spread of social networks. For its significant practical applications, the influence maximisation problem has been widely used in many fields, such as network marketing and rumour control. However, most existing algorithms tend to select accuracy or efficiency to optimise, which leads to their poor performance. Therefore, a Three-step Fast Selection algorithm for Influence Maximisation (TsFSIM) is proposed in this paper. Firstly, a new method to evaluate nodes' influence spread is proposed, called Influence Estimation Value. Influence Estimation Value (IEV) combines the node's and its neighbours' degree to estimate its influence. This can improve the efficiency of our algorithm. Afterwards, based on IEV, a three-stage filtering strategy is proposed. This strategy can improve the accuracy of our algorithm greatly. Finally, experimental results on seven real-world networks show that the proposed method is more accurate than other methods while keeping competitive efficiency.