Book Chapter10.1007/978-3-319-68612-7_18
Link Enrichment for Diffusion-Based Graph Node Kernels
Dinh Tran-Van,Alessandro Sperduti,Fabrizio Costa +2 more
- 11 Sep 2017
- pp 155-162
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TL;DR: Here, the notion of link enrichment is introduced, that is, performing link prediction in order to improve the performance of diffusion-based kernels to solve gene-disease association problems.
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Abstract: The notion of node similarity is key in many graph processing techniques and it is especially important in diffusion graph kernels. However, when the graph structure is affected by noise in the form of missing links, similarities are distorted proportionally to the sparsity of the graph and to the fraction of missing links. Here, we introduce the notion of link enrichment, that is, performing link prediction in order to improve the performance of diffusion-based kernels. We empirically show a robust and large effect for the combination of a number of link prediction and a number of diffusion kernel techniques on several gene-disease association problems.
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Graph Kernel Attention Transformers
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Assignment of structural domains in proteins using diffusion kernels on graphs
TL;DR: In this paper , a protein domain decomposition method based on diffusion kernels on protein graphs is proposed, which is able to offer alternative partitionings for the same structure which is in line with the subjective definition of protein domain.
Assignment of structural domains in proteins using diffusion kernels on graphs
TL;DR: In this paper , a protein domain decomposition method based on diffusion kernels on protein graphs is proposed, which is able to offer alternative partitionings for the same structure which is in line with the subjective definition of protein domain.
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- 01 Jan 2017
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