Comments (2)
An essential part of the node2vec algorithm is the walk generation, where a random walk is started from nodes in the graph.
If you trained a model on a specific graph and want to resume it after adding more nodes you essentially invalidate all the random walks that could have potentially touch the new nodes.
So to answer you question, this actually can't be done because of the nature of the algorithm rather than the implementation
from node2vec.
Makes sense.
Thanks.
from node2vec.
Related Issues (20)
- How can node2vec use GPU HOT 1
- too slow and stuck for some reason HOT 9
- reproducability HOT 2
- failing simple clustering of synthetic networks HOT 2
- Error in model creation node2vec HOT 1
- Cannot import Node2Vec HOT 5
- A faster implement HOT 1
- Train vs Inference methods HOT 6
- [Question] about directed graph and weight HOT 2
- There may be a small problem with the walking length. HOT 1
- Interpretation of weights HOT 2
- model save and loading examples HOT 2
- Are there any ways to initialize node vector using pretrained embeddings? HOT 1
- Crash when edges are not initialised with a weight attribute HOT 8
- can same temp directory be used for different graphs in for loop training? HOT 4
- _precompute_probabilities parallel execution HOT 2
- import error HOT 3
- segmentation fault HOT 5
- Error installing gensim in poetry env on mac m1 HOT 3
- How to set parameters for a collection small disconnected graphs HOT 2
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from node2vec.