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Graph neural networks (GNNs) are proficient machine learning models in handling irregularly structured data. Nevertheless, their generic formulation falls short when applied to the analysis of brain connectomes in Alzheimer’s Disease (AD), necessitating the incorporation of domain-specific knowledge to achieve optimal model performance. The integration of AD-related expertise into GNNs presents a significant challenge. Current methodologies reliant on manual design often demand substantial expertise from external domain specialists to guide the development of novel models, thereby consuming considerable time and resources. To mitigate the need for manual curation, this paper introduces a novel self-guided knowledge-infused multimodal GNN to autonomously integrate domain knowledge into the model development process. We propose to conceptualize existing domain knowledge as natural language, and devise a specialized multimodal GNN framework tailored to leverage this uncurated knowledge to direct the learning of the GNN submodule, thereby enhancing its efficacy and improving prediction interpretability. To assess the effectiveness of our framework, we compile a comprehensive literature dataset comprising recent peer-reviewed publications on AD. By integrating this literature dataset with several real-world AD datasets, our experimental results illustrate the effectiveness of the proposed method in extracting curated knowledge and offering explanations on graphs for domain-specific applications. Furthermore, our approach successfully utilizes the extracted information to enhance the performance of the GNN.
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LINC: Knowledge Mobilization Framework for Knowledge Brokers
Knowledge brokers play an essential role in bridging research and practice and mobilizing knowledge. Yet, literature offers little guidance and few examples for people and organizations engaging with educational systems in this capacity. Drawing on literature and our extensive knowledge brokerage experience, we address this gap and introduce the Learn, Illuminate, Nucleate, and Communicate (LINC) knowledge mobilization framework for knowledge brokers. We explain framework components and give examples from our project.
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- Award ID(s):
- 2101341
- PAR ID:
- 10609116
- Publisher / Repository:
- Repository of the International Society of Learning Sciences
- Date Published:
- Page Range / eLocation ID:
- 2759 to 2761
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.22318/icls2025.120465
