超越推特关注图

G. Amati, Simone Angelini, M. Bianchi, Gianmarco Fusco, G. Gambosi, Giancarlo Gaudino, G. Marcone, Gianlu a Rossi, P. Vocca
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引用次数: 5

摘要

研究社交网络平台生成的图的拓扑性质,无论从社会的角度还是从信息的角度都具有重要意义。此外,它对设计新应用程序和改进现有服务有很大的影响。令人惊讶的是,研究界似乎主要把精力集中在研究最直观、最明确的图表上,比如Twitter平台的追随者图表,或者Facebook的朋友图表:因此,许多有价值的信息仍然被隐藏,等待被探索和利用。本文介绍了一种新的Twitter用户行为图建模方法:提及图。然后,我们展示了如何从Twitter流开始轻松构建这种图的实例,并报告了一个实验的结果,该实验旨在通过使用一些标准的社交网络分析指标,将提议的图与文献中已经分析的其他图进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Moving beyond the Twitter follow graph
The study of the topological properties of graphs derived from social network platforms has a great importance both from the social and from the information point of view; furthermore, it has a big impact on designing new applications and in improving already existing services. Surprisingly, the research community seems to have mainly focused its efforts just on studying the most intuitive and explicit graphs, such as the follower graph of the Twitter platform, or the Facebook friends' graph: consequently, a lot of valuable information is still hidden and it is waiting to be explored and exploited. In this paper we introduce a new type of graph modeling behavior of Twitter users: the mention graph. Then we show how to easily build instances of this graphs starting from the Twitter stream, and we report the results of an experimentation aimed to compare the proposed graph with other graphs already analyzed in the literature, by using some standard social network analysis metrics.
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