arXiv (All)
open access
Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents
Consistency within a document is a crucial feature indicative of its quality. Recently, within the vast amount of information produced across various media, there exists a significant number of low-quality documents that either lack internal consistency or contain content utterly unrelated to their
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- KITE: A Tri-Modal Transformer Integrating Text, Images, and Knowledge Graphs for Fake News Detection#1011127
- From Phase Transition to Systemic Failure: A Decoupled Analytics Framework for GNN Robustness#1115612
- HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs#793418
- HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs#789414
- ChatGPT Images 2.5 in the Wild: A Launch-Period Dataset and Detector Evaluation#1194126
Record · ID 1356686
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive