Finding Critical Users in Social Communities via Graph Convolutions

Finding critical users whose existence keeps a social community cohesive and large is an important issue in social networks. In the literature, such criticalness of a user is measured by the number of followers who will leave the community together when the user leaves. By taking a social community...

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Veröffentlicht in:	IEEE transactions on knowledge and data engineering 2023-01, Vol.35 (1), p.456-468
Hauptverfasser:	Zhao, Kangfei, Zhang, Zhiwei, Rong, Yu, Yu, Jeffrey Xu, Huang, Junzhou
Format:	Artikel
Sprache:	eng
Schlagworte:	<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> k</tex-math> <mml:math> <mml:mi>k</mml:mi> </mml:math> <inline-graphic xlink:href="yu-ieq9-3089763.gif" xlink:type="simple"/> </inline-formula> </named-content>-core collapsion Algorithms Complexity theory graph neural network Greedy algorithms Inference Neural networks Nodes Sampling Social network analysis Social networking (online) Social networks Task analysis Testing Topology Training
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