3,766 research outputs found
A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics
Graph neural networks (GNNs) have demonstrated a significant boost in
prediction performance on graph data. At the same time, the predictions made by
these models are often hard to interpret. In that regard, many efforts have
been made to explain the prediction mechanisms of these models from
perspectives such as GNNExplainer, XGNN and PGExplainer. Although such works
present systematic frameworks to interpret GNNs, a holistic review for
explainable GNNs is unavailable. In this survey, we present a comprehensive
review of explainability techniques developed for GNNs. We focus on explainable
graph neural networks and categorize them based on the use of explainable
methods. We further provide the common performance metrics for GNNs
explanations and point out several future research directions
Knowledge-based Biomedical Data Science 2019
Knowledge-based biomedical data science (KBDS) involves the design and
implementation of computer systems that act as if they knew about biomedicine.
Such systems depend on formally represented knowledge in computer systems,
often in the form of knowledge graphs. Here we survey the progress in the last
year in systems that use formally represented knowledge to address data science
problems in both clinical and biological domains, as well as on approaches for
creating knowledge graphs. Major themes include the relationships between
knowledge graphs and machine learning, the use of natural language processing,
and the expansion of knowledge-based approaches to novel domains, such as
Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages
with 3 table
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