Graph Representation Learning via Contrasting Cluster Assignments

With the rise of contrastive learning, unsupervised graph representation learning (GRL) has shown strong competitiveness. However, existing graph contrastive models typically either focus on the local view of graphs or take simple considerations of both global and local views. This may cause these m...

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Veröffentlicht in:	IEEE transactions on cognitive and developmental systems 2024-06, Vol.16 (3), p.912-922
Hauptverfasser:	Zhang, Chun-Yang, Yao, Hong-Yu, Chen, C. L. Philip, Lin, Yue-Na
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Clustering Clustering algorithms Clusters Contrastive learning graph data mining graph representation learning (GRL) Graph representations Graphical representations Machine learning Nodes Prototypes Representation learning Social networking (online) Task analysis Topology Unsupervised learning
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