异构社会网络的学习影响

IF 2.8 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Data Mining and Knowledge Discovery Pub Date : 2012-03-03 DOI:10.1007/s10618-012-0252-3

Lu Liu, Jie Tang, Jiawei Han, Shiqiang Yang

引用次数: 89

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Learning influence from heterogeneous social networks

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来源期刊

Data Mining and Knowledge Discovery 工程技术-计算机：人工智能

CiteScore

10.40

自引率

4.20%

发文量

审稿时长

10 months

期刊介绍： Advances in data gathering, storage, and distribution have created a need for computational tools and techniques to aid in data analysis. Data Mining and Knowledge Discovery in Databases (KDD) is a rapidly growing area of research and application that builds on techniques and theories from many fields, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modelling, data warehousing and OLAP, optimization, and high performance computing.