Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: While graphs with continuous node attributes arise in many applications, state-of-the-art graph kernels for comparing continuous-attributed graphs suffer from a high runtime complexity. For instance, the popular shortest path kernel scales as O(n4), where n is the number of nodes. ,  present a class of graph kernels with computational complexity O(n2(m + log n + δ2 + d)), where δ is the graph diameter, m is the number of edges, and d is the dimension of the node attributes. Due to the sparsity and small diameter of real-world graphs, these kernels typically scale comfortably to large graphs. In our experiments, the presented kernels outperform state-of-the-art kernels in terms of speed and accuracy on classification benchmark datasets.
['Aasa Feragen', 'Niklas Kasenburg', 'Jens Petersen', 'Marleen de Bruijne', 'Karsten M. Borgwardt']
I am writing a research paper on a idea Scalable kernels for graphs with continuous attributes
[{"from": "human", "value": "I am writing a research paper on a idea Scalable kernels for graphs with continuous attributes"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: While graphs with continuous node attributes arise in many applications, state-of-the-art graph kernels for comparing continuous-attributed graphs suffer from a high runtime complexity. For instance, the popular shortest path kernel scales as O(n4), where n is the number of nodes. ,  present a class of graph kernels with computational complexity O(n2(m + log n + \u03b42 + d)), where \u03b4 is the graph diameter, m is the number of edges, and d is the dimension of the node attributes. Due to the sparsity and small diameter of real-world graphs, these kernels typically scale comfortably to large graphs. In our experiments, the presented kernels outperform state-of-the-art kernels in terms of speed and accuracy on classification benchmark datasets."}]