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Updated: Oct 2, 2025

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Dynamic Heterogeneous User Generated Contents-Driven Relation Assessment via Graph Representation Learning.

Ru Huang1, Zijian Chen1, Jianhua He2

  • 1School of Information Science & Engineering, East China University of Science and Technology, Shanghai 200237, China.

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|February 26, 2022
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Summary
This summary is machine-generated.

This study introduces a new framework for analyzing user-generated content (UGC) by considering relationships and community structures. The community-aware dynamic heterogeneous graph embedding (CDHNE) model effectively assesses content quality and relationships.

Keywords:
community detectiongraph representation learningrelation assessmentuser-generated contents

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Area of Science:

  • Computer Science
  • Data Science
  • Network Analysis

Background:

  • Online platforms face challenges with varying quality of user-generated content (UGC).
  • Existing content analysis methods often overlook the interrelationships within UGC.
  • Complex non-Euclidean structured problems require advanced analytical approaches.

Purpose of the Study:

  • To propose a novel framework, CDHNE, for relationship assessment in UGC.
  • To mine heterogeneous information, latent community structure, and dynamic characteristics from UGC.
  • To address the limitations of current content analysis methods by integrating network analysis.

Main Methods:

  • Utilizing Markov-chain-based metapaths for extracting heterogeneous content and semantics from UGC.
  • Employing an edge-centric attention mechanism for localized feature aggregation.
  • Developing an encoder-decoder structure with recurrent memory units to capture temporal dynamics for relation assessment.

Main Results:

  • CDHNE demonstrates superior performance compared to baseline methods across four real-world datasets.
  • The model achieves comprehensive node representation, enhancing relation assessment accuracy.
  • Experimental results validate the effectiveness of CDHNE in uncovering global structures and temporal patterns.

Conclusions:

  • CDHNE offers a robust solution for analyzing complex UGC by integrating content, community, and dynamic features.
  • The framework effectively breaks down barriers between traditional UGC analysis and abstract network analysis.
  • This approach provides a significant advancement in cross-domain decision-making systems dealing with UGC.