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The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks
Weiwei Gu1, Li Gong1, Xiaodan Lou1
1School of Systems Science, Beijing Normal University, Beijing, 100875, P. R. China.
Scientific Reports
|October 15, 2017
Summary
This study reveals that random walk network embedding relies on an underlying metric structure within open-flow networks. Understanding this structure enhances network embedding algorithms and identifies new applications.
Area of Science:
- Computer Science
- Network Science
- Data Mining
Background:
- Network embedding encodes network structures into numerical vectors for various applications.
- Random walk-based algorithms are popular for network embedding due to efficiency and accuracy.
- Existing random walk methods lack theoretical explanations and transparency.
Purpose of the Study:
- To propose an open-flow network model approach to explain random walk-based network embedding.
- To reveal the underlying flow structure and metric space of random walk strategies.
- To deepen the understanding of network embedding algorithms.
Main Methods:
- Utilizing an open-flow network model.
- Analyzing the latent metric structure defined on the network.
- Investigating different random walk strategies.
Main Results:
- Demonstrated that random walk embedding is fundamentally based on a latent metric structure.
- Provided a theoretical explanation for random walk-based network embedding.
- Identified the connection between flow structure and embedding properties.
Conclusions:
- The open-flow network model offers a new perspective on network embedding.
- This research deepens the theoretical understanding of random walk embedding algorithms.
- The findings can lead to novel applications in network analysis and machine learning.
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