Label-dependent Feature Extraction in Social Networks for Node Classification
提出了一種基於社會網絡特徵提取的網絡內分類方法。該方法提供了結合網絡結構信息和分配給節點的類標籤來計算的新特性。研究了不同特徵對分類性能的影響。在真實數據上的實驗表明，該方法生成的特徵可以顯著提高分類精度。 Introduction 有一些應用和研究方法，特別是與社交網絡相關的應用和研究方法，能夠產生相互連接的對象標籤之間依賴的數據，稱爲關係自相關。根據這些連接，應該向分類過程中添加額外的輸入
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相關文章
- 1. Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
- 2. ArnetMiner: Extraction and Mining of Academic Social Networks
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- 4. Graph Neural Networks for Social Recommendation
- 5. Graph based feature extraction and hybrid classification approach for facial expression recognition
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