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We introduce a new method for temporal pattern analysis of scientific collaboration networks We investigate in particular virus research activities through five epidemic or pandemic outbreaks in the recent two decades and in the ongoing pandemic with COVID-19 Our method embodies two innovative components The first is a simple model of temporal collaboration networks with time segmented in publication time and convolved in citation history, to effectively capture and accommodate collaboration activities at mixed time scales The second component is the novel use of graphlets to encode topological structures and to detect change and persistence in collaboration activities over time We discover in particular two unique and universal roles of bi-fork graphlet in (1) identifying bridges among triangle clusters and (2) quantifying grassroots as the backbone of every collaboration network We present a number of intriguing patterns and findings about the virus-research activities
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We introduce a new method for temporal pattern analysis of scientific collaboration networks We investigate in particular virus research activities through five epidemic or pandemic outbreaks in the recent two decades and in the ongoing pandemic with COVID-19 Our method embodies two innovative components The first is a simple model of temporal collaboration networks with time segmented in publication time and convolved in citation history, to effectively capture and accommodate collaboration activities at mixed time scales The second component is the novel use of graphlets to encode topological structures and to detect change and persistence in collaboration activities over time We discover in particular two unique and universal roles of bi-fork graphlet in (1) identifying bridges among triangle clusters and (2) quantifying grassroots as the backbone of every collaboration network We present a number of intriguing patterns and findings about the virus-research activities © 2020 IEEE
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IEEE_High_Perform._Extrem._Comput._Conf.,_HPEC
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Using Graphlet Spectrograms for Temporal Pattern Analysis of Virus-Research Collaboration Networks
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