인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Graph Neural Networks have gained popularity over the past few years. Their ability to model relationships between entities of the same and different kind, represent molecules, model flow etc. have made them a go to tool for researchers. However, owing to the abstract nature of graphs, there exists no ideal transformation to represent nodes and edges in the euclidean space. Moreover, GNNs are highly susceptible to adversarial attacks. However, a gradient based attack based on latent space embeddings does not exist in the GNN literature. Such attacks, classified as white box attacks, tamper with latent space representation of graphs without creating any noticeable difference in the overall distribution. Developing and testing GNN models based on such attacks on graph classification tasks would enable researchers to understand and develop stronger and more robust classification systems. Further, adversarial attack tests in the GNN literature have been performed on weaker, less representative neural network architectures. In order to tackle these gaps in literature, we propose a white box gradient based attack developed from contrastive latent space representations. Further, we develop a strong base(victim) learning spectral and spatial properties of graphs with consideration of isomorphic properties. We experimentally validate this model on 4 benchmark datasets in the molecular property prediction literature where our model outperformed over 75% of all LLM-based architectures. On attacking this model with our proposed adversarial attack strategy, the overall performance drops at an average of 25% thereby clearing a few gaps in the existent literature. The code for our paper can be found at https://github.com/Deceptrax123/An-edge-sensitivity-based-gradient-attack-on-GIN-for-inductive-problems.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
