Comments (1)
giannisnik
commented on June 1, 2024
2
Hi @sbhttchryy . Thank you for your interest in our library.
With regards to your question, the answer is yes, you can also perform regression tasks. You still create the kernel matrices using some graph kernel, and then instead of the SVM classifier, you pass these matrices on to the SVR algorithm (see here: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html). Note that you still need to set the "kernel" attribute of SVR equal to "precomputed".
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Related Issues (20)
- Print <class 'grakel.graph.Graph'>
- The Weisfeiler-Lehman Edge Kernel
- fetch_dataset not working HOT 1
- Graph hopper kernel unable to transfom the data
- Edge labels not working for custom dataset HOT 1
- Fingerprint dataset not accessible using fetch_dataset function HOT 13
- NaN error when using Random walk kernel on certain datasets HOT 3
- I don't know how to use my own data for input HOT 5
- MultiDiGraph: graph_from_networkx() ValueError HOT 1
- modified random walk kernel giving all 1 scores for normalize=true HOT 1
- Difference between ShortestPath and ShortestPathAttr HOT 2
- Generating graphs from dense/numpy matrices HOT 1
- Graph.clone() or Graph.copy() HOT 3
- Explicit graph kernels HOT 2
- Graphlet kernel HOT 1
- NeighborhoodSubgraphPairwiseDistance kernel returns diagonal elements less than 1 HOT 5
- One vs many comparison for WeisfeilerLehman; `.transform` gives `TypeError: each element of X must have either a graph with...` HOT 1
- RWK matrix returns by fit_transform by RandomWalkLabeled() contains only 1.0 HOT 7
- Error when using EdgeHistogram HOT 1
- Can't install on Windows 10, keep getting same error HOT 2
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