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Measuring the Inspiration Rate of Topics in Bibliographic Networks

机译：在书目网络中衡量主题的启发率

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摘要

Information diffusion is a widely-studied topic thanks to its applications to social mediaetwork analysis, viral marketing campaigns, influence maximization and prediction. In bibliographic networks, for instance, an information diffusion process takes place when some authors, that publish papers in a given topic, influence some of their neighbors (coauthors, citing authors, collaborators) to publish papers in the same topic, and the latter influence their neighbors in their turn. This well-accepted definition, however, does not consider that influence in bibliographic networks is a complex phenomenon involving several scientific and cultural aspects. In fact, in scientific citation networks, influential topics are usually considered those ones that spread most rapidly in the network. Although this is generally a fact, this semantics does not consider that topics in bibliographic networks evolve continuously. In fact, knowledge, information and ideas are dynamic entities that acquire different meanings when passing from one person to another. Thus, in this paper, we propose a new definition of influence that captures the diffusion of inspiration within the network. We propose a measure of the inspiration rate called inspiration rank. Finally, we show the effectiveness of our measure in detecting the most inspiring topics in a citation network built upon a large bibliographic dataset.
机译：信息传播由于其在社交媒体/网络分析，病毒式营销活动，影响力最大化和预测中的应用而成为广泛研究的话题。例如，在书目网络中，当某些作者在给定主题上发表论文时，影响到其某些邻居（共同作者，引用作者，合作者）以同一主题发表论文，而后者的影响则发生了信息传播过程。他们的邻居。但是，这个公认的定义并不认为书目网络中的影响是一个复杂的现象，涉及多个科学和文化方面。实际上，在科学引文网络中，有影响力的主题通常被认为是在网络中传播最快的主题。尽管这通常是事实，但这种语义并不认为书目网络中的主题会不断发展。实际上，知识，信息和思想是动态的实体，当从一个人传递到另一个人时，它们会获得不同的含义。因此，在本文中，我们提出了一种影响的新定义，该定义捕获了网络内灵感的扩散。我们提出了一种衡量吸气率的方法，称为吸气率等级。最后，我们展示了我们的措施在基于大型书目数据集构建的引文网络中检测最具启发性的主题方面的有效性。

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