The prediction for information diffusion on social networks has great practical significance in marketing and public opinion control. It aims to predict the individuals who will potentially repost the message on the social network. One type of method is based on demographics, complex networks and other prior knowledge to establish an interpretable model to simulate and predict the propagation process, while the other type of method is completely data-driven and maps the nodes to a latent space for propagation prediction. Existing latent space design and embedding methods lack consideration for the intervene among users. In this paper, we propose an independent asymmetric embedding method to embed each individual into one latent influence space and multiple latent susceptibility spaces. Based on the similarity between information diffusion and heat diffusion phenomenon, the heat diffusion kernel is exploited in our model and establishes the embedding rules. Furthermore, our method captures the co-occurrence regulation of user combinations in cascades to improve the calculating effectiveness. The results of extensive experiments conducted on real-world datasets verify both the predictive accuracy and cost-effectiveness of our approach.
翻译:社会网络信息传播预测在营销和公众舆论控制方面具有重大的实际意义,其目的是预测有可能在社交网络上重新发布信息的个人。一种方法基于人口统计学、复杂的网络和其他先前知识,以建立模拟和预测传播过程的可解释模型,而另一种方法则完全以数据为动力,将节点绘制成传播预测的潜在空间。现有的潜伏空间设计和嵌入方法缺乏用户干预的考虑。在本文中,我们提议了一种独立的不对称嵌入方法,将每个人嵌入一个潜在影响空间和多种潜在易感性空间。基于信息传播和热扩散现象之间的相似性,热扩散内核在我们的模型中被利用,并确立了嵌入规则。此外,我们的方法还抓住了串联中用户组合的共同规则,以提高计算效率。在现实世界数据集上进行的广泛实验的结果证实了我们方法的预测准确性和成本效益。