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In the field of graph machine learning, we need some algorithms to vectorize the graph.
For example, random walk algorithm samples the graph and produces a series of walk sequences, which are then embedding vectorized.
It can be used for graph classification, graph prediction and other application scenarios.
The whole process is roughly as follows:
Refer to paper [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba]
Here are related papers:
DeepWalk: Online Learning of Social Representations
node2vec: scalable feature learning for networks
The text was updated successfully, but these errors were encountered:
Feature Description (功能描述)
In the field of graph machine learning, we need some algorithms to vectorize the graph.
For example, random walk algorithm samples the graph and produces a series of walk sequences, which are then embedding vectorized.
It can be used for graph classification, graph prediction and other application scenarios.
The whole process is roughly as follows:
Refer to paper [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba]
Here are related papers:
The text was updated successfully, but these errors were encountered: