(A) node embedding enhancement framework to mitigate cold start problem in GNN그래프 신경망에서의 콜드스타트 문제 완화를 위한 노드 임베딩 강화 프레임워크

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The cold start problem is a significant challenge in Graph Neural Network (GNN)-based models for tasks like recommender systems and link prediction. Limited interaction nodes, known as "cold nodes", hinder accurate embedding formation, leading to performance degradation. Existing approaches often rely on complex calculations or separate side information, which may not be practical in real-world scenarios. To address this, we propose a Node Embedding Enhancement Framework (NEEF), a framework that focuses on improving node representations to mitigate the cold start problem. Inspired curriculum learning, framework generates reliable node embeddings from a subgraph of "warm nodes". These embeddings are then integrated into the graph's node features, improving discriminate power. Extensive experiments on real-world datasets demonstrate the effectiveness of our framework in mitigating cold start effects. It consistently outperforms the state-of-the-art methods, significantly improving performance. The framework seamlessly integrates into existing GNN architectures, enabling broad application without major modifications. Our improved embedding framework advances GNN-based models, addressing the cold start problem and enhancing their capabilities. Its practicality and effectiveness have the potential to enhance real-world applications that rely on graph-based data without adding side information.
Advisors
이문용researcher
Description
한국과학기술원 :데이터사이언스대학원,
Publisher
한국과학기술원
Issue Date
2024
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 데이터사이언스대학원, 2024.2,[iv, 29 p. :]

Keywords

그래프신경망▼a콜드스타트▼a추천시스템▼a링크예측▼a커리큘럼 러닝; Cirriculum learning; Graph neural network▼aCold start▼aRecommender system▼aLink prediction

URI
http://hdl.handle.net/10203/321427
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1096212&flag=dissertation
Appears in Collection
IE-Theses_Master(석사논문)
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