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Automatic Identification of Dendritic Branches and their Orientation

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

Active link selection for efficient semi-supervised community detection.

Liang Yang¹, Di Jin², Xiao Wang²

¹1] State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China [2] School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China.

Scientific Reports

|March 13, 2015

Summary

This summary is machine-generated.

This study introduces an active link selection framework for semi-supervised community detection. It efficiently uses limited supervised information by selecting uncertain links and disconnecting inter-community edges, outperforming existing methods.

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Area of Science:

Network Science
Data Mining
Machine Learning

Background:

Traditional topology-based community detection methods have limitations.
Existing semi-supervised methods often require extensive, hard-to-obtain supervised information.
The critical factors for improving semi-supervised performance are not well understood.

Purpose of the Study:

To develop a framework for efficient utilization of supervised information in community detection.
To reduce the demand for expensive or difficult-to-acquire supervised data.
To enhance the performance of semi-supervised community detection algorithms.

Main Methods:

Proposing an active link selection framework that identifies uncertain and informative links for labeling.
Implementing a strategy to disconnect likely inter-community edges to improve efficiency.
Connecting uncertain nodes to their community hubs to sharpen the adjacency matrix's block structure.

Main Results:

The proposed approach significantly outperforms existing methods in supervised information efficiency.
Achieves similar performance to original semi-supervised approaches using only ~13% of the supervised information.
Demonstrates effectiveness on both synthetic and real-world network data.

Conclusions:

Active link selection is a more efficient strategy for utilizing supervised information in community detection.
The framework effectively sharpens network block structures with minimal labeled data.
This method offers a practical solution for community detection in data-scarce environments.