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This is a benchmark dataset for evaluating hybrid-graph (hypergraph and hierarchical graph) learning algorithms. It contains: 21 real-world higer-order graphs from the domains of biology, social media, and wikipedia For accessing additional functionalities, please access through Project Page Built-in functionalities for preprocessing hybrid-graphs A framework to easily train and evaluate Graph Neural Networks
Machine Learning, Hypergraph, Graph Neural Networks, Hybrid-graph
Machine Learning, Hypergraph, Graph Neural Networks, Hybrid-graph
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 96 |

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