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Title: | Your Tribe Decides Your Vibe: Analyzing Local Popularity in the US Patent Citation Network |
Authors: | Narang, Nishit |
Keywords: | Computer Science Social and Information Networks Neural networks |
Issue Date: | Jun-2021 |
Publisher: | ARXIV |
Abstract: | In many networks, the indegree of a vertex is a measure of its popularity. Past research has studied indegree distributions treating the network as a whole. In the US Patent citation network (USPCN), patents are classified into categories and subcategories. A natural question arises: How do patents gather their popularity from various (sub)categories? We analyse local indegree distributions to answer this question. The citation (indegree) of a patent within the same category indicates its internal popularity, while a cross-category citation indicates its external popularity. We analyze the internal and external indegree distributions at each level of USPCN hierarchy to learn how the internal and external popularity of patents varies across (sub)categories. We find that all (sub)categories have local preferences that decide internal and external patents' popularities. Different patents are popular in different groups: Groups C1, C2 and C3 may not agree on popular patents in C1. In general, patent popularity appears to be a highly local phenomenon with subcategories (not even categories) deciding their own popular patents independent of the other (sub)categories |
URI: | https://arxiv.org/abs/2106.01148 http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8677 |
Appears in Collections: | Department of Computer Science and Information Systems |
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