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Exploring the Value of Nodes with Multicommunity Membership for Classification with Graph Convolutional Neural Networks
Michael Hopwood1  AlexanderV. Mantzaris1  Phuong Pho1 
[1] Department of Statistics and Data Science, University of Central Florida, Orlando, FL 32816, USA;
关键词: graph convolutional neural networks;    social networks;    network science;    citation networks;    community labeling;   
DOI  :  10.3390/info12040170
来源: DOAJ
【 摘 要 】

Sampling is an important step in the machine learning process because it prioritizes samples that help the model best summarize the important concepts required for the task at hand. The process of determining the best sampling method has been rarely studied in the context of graph neural networks. In this paper, we evaluate multiple sampling methods (i.e., ascending and descending) that sample based off different definitions of centrality (i.e., Voterank, Pagerank, degree) to observe its relation with network topology. We find that no sampling method is superior across all network topologies. Additionally, we find situations where ascending sampling provides better classification scores, showing the strength of weak ties. Two strategies are then created to predict the best sampling method, one that observes the homogeneous connectivity of the nodes, and one that observes the network topology. In both methods, we are able to evaluate the best sampling direction consistently.

【 授权许可】

Unknown   

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