社交网络中求最小正影响支配集的改进算法

An Improved Algorithm for Finding Minimum Positive Influence Dominating Sets in Social Networks

网络中求解最小正影响支配集的问题已经被证明是NP难问题,且已有性能较好的贪心求解算法.通过分析现有的贪心近似算法(Wang-Greedy)和贪心启发式算法(Raei-Greedy),融合其贪心策略,提出了1个改进的贪心近似算法(Hybrid-Greedy).理论分析表明,Hybrid-Greedy仍保持Wang-Greedy的近似比性能和时间复杂度.在一些较大规模的真实社交网络实例中的实验研究表明,Hybrid-Greedy在这些社交网络中所得解的质量较Wang-Greedy和Raei-Greedy有明显提高.

The problem of finding the minimum positive influence dominating set in a given network has been proved to be NP-hard,and there are greedy algorithms with good performance to solve it. An existing greedy approximation algorithm（ Wang-Greedy） and a heuristic algorithm（ Raei-Greedy） are analyzed. Accordingly,an improved greedy approximation algorithm（ Hybrid-Greedy） to find the minimum positive influence dominating sets in social networks is proposed. It is shown theoretically that hybrid-Greedy remains the same approximation ratio and time complexity as Wang-Greedy. Experimental results on some large real-world social network instances show that Hybrid-Greedy yields better solutions than Wang-Greedy and Raei-Greedy for these social network instances.