计算机科学 ›› 2016, Vol. 43 ›› Issue (9): 66-70.doi: 10.11896/j.issn.1002-137X.2016.09.012
• 2015 年第三届CCF 大数据学术会议 • 上一篇 下一篇
徐文涛,刘锋,朱二周
XU Wen-tao, LIU Feng and ZHU Er-zhou
摘要： 微博凭借其即时发布、实时传播、简便易用的特点逐渐成为最为主流的自媒体平台。用户影响力评价是微博社交网络中基本而又重要的问题,它对于优化与推动社会信息传播来说有着重要意义。以新浪微博为实验对象,通过综合考虑微博用户关系网络特性和用户行为,结合MapReduce编程计算模型,提出了一种基于MapReduce的新型用户影响力排名算法——QRank。在Hadoop平台上的实验结果表明,QRank算法具有良好的可扩展性,能够有效结合微博用户关系网络与行为特性,从而更加真实与充分地反映用户的实际影响力。
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