Accuracy: 49.75%
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Graph Classification
Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.
<span style="color:grey; opacity: 0.6">( Image credit: Hierarchical Graph Pooling with Structure Learning )</span>
개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.
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데이터셋
76개논문
Accuracy(10-fold): 81.13
Accuracy: 52.88%
Accuracy: 41.27%
Accuracy: 52.11%
Accuracy: 57.22%
Accuracy(10-fold): 75.21
Accuracy(10-fold): 78.11
Accuracy: 96.41%
Accuracy: 55.1%
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