微博社交网络alpha用户识别的关键理论与算法

在微博社交网络信息碎片化的环境下，面对既要加速正面信息的传播扩散，又要阻止负面、虚假信息的蔓延，这样的两难困境，在千万乃至上亿用户中识别出最有效率的信息传播者，即alpha用户，是解决这个两难问题的最根本的办法。为此本课题利用最适化学习思想、基于节点互动性框架，对微博社交网络中alpha用户辨识问题，从理论、算法、与应用三方面，给出全面、彻底的解决方案。具体研究内容包括增量式理论引擎的构建，节点互动指标的定制式挖掘，超大规模复杂情况下辨识算法族的设计，以及品牌营销与声誉危机公关等应用问题的研究。最适化学习思想有助于针对节点互动指标与辨识概率间存在的复杂非线性关系获得极限辨识精度；互动性框架克服了现有识别方法完全依赖节点属性与网络整体结构而导致的可靠样本难以获取的不足，主要聚焦于节点间的互动信息这一局部可见结构。同时采用开源式研究过程，促进课题高质量完成，也带动国内该领域整体研究水平的提升。

The information is fragmented in the microblog social network, so identifying the alpha users(namely the most efficient spreader ) in the millions and even hundreds of millions of users is the most fundamental method to deal with the dilemma problem that accelerating the dissemination of positive information diffusion as well as preventing the dissemination of the negative and false information diffusion. Based on the optimization application method and interaction framework, we provide the complete and thorough theory, algorithms, and application solutions of identifying the alpha users. The specific research contents include building incremental theory engine, customized mining node interaction indicators, designing identification algorithm family in the ultra-large-scale complex cases, and studying the brand marketing and public relations management for reputation crisis. The optimization application method contributes to obtain the ultimate identification accuracy according to the complex non-linear relationship between node interaction indicators and identification probability. Interactive framework can overcome the deficiency of the existing identification methods which rely entirely on node attributes and network overall structure thus result in the shortage of the high reliability sample. The framework mainly focuses on the interaction information between nodes,which is the partial visible structure of network. Meanwhile, we use an open-source research process to promote the high quality accomplishment of the project, and also to enhance the overall level of research in this field in China.