社交网络话题传播模型剪枝策略研究

The Research on Pruning Strategies Topic Propagation Model of Social Network

在进行社交网络话题传播时,随着数据量的不断增大,传播模型在进行传播模拟时所花销的时间更多,程序运行所占用存储空间也更大。然而,在实际的话题传播过程中,大多数话题集中在某些关键节点上,且相当一部分节点对话题的传播没有太大的影响。因此,如果在进行话题传播时,能够剪掉社交网络中的某些传播节点,这不仅能够减少程序的运行时间,而且能够降低数据所占用的存储空间。针对上述问题,设计了两种新颖的图剪枝算法来减少社交网络中的节点数量。本文所提出的两种算法是将推荐系统的思想引入到社交网络传播模型的剪枝策略研究中,具有一定的新颖性。通过实验对比分析了不同剪枝策略对传播模型的效果、所占空间、运行时间以及图的健壮性的影响。

With the spreading of topics in the social network,topic models would spent more time and more storage space with the increase of the size of data. However,most topics focus on some key nodes and parts of nodes have no significant effect on topic propagation in the real process of topic propagation. If some nodes could be reasonably cut in the social network during the spread of topics,the runtime of the program and the storage space both would be reduced. To solve the above problem,the paper designs two novel graph pruning algorithm to reduce the number of nodes in the social network.The two algorithms presented in this paper introduced the thought of recommend system into the research on pruning strategy of topic propagation models and have a certain novelty. With the analysis and comparison,the paper analyzes the impact of different pruning strategies of propagation model on the effectiveness,the space,running time and the robustness of the graph.