Centers, labs, digital archives, literature review, etc.
Graphical Neural Networks (GNNs) are a family of neural networks that can operate naturally on graphically structured data. They provide an effective way to do node-level, edge-level, and graph-level prediction tasks by extracting and exploiting features from the underlying graph.
Scholarly networks, built from academic metadata, have proved powerful tools in these tasks. Its nodes usually denotes academic entities (concept, paper, venue, researcher, institution, etc.) and links represent relationships such as citation, co-authorship, or co-word.
Science mapping aims to reveal the structure and dynamics of academic research by providing a holistic view of the knowledge domain, i.e., the distribution and connections of academic units, such as topics, publications, journals, and researchers.
Raw data → structured data (maintain and evolve) → graph algorithm and model → application
Cluster users with unsupervised learning algorithms K-means, DBSCAN, Agglomerative, and EM-GMM. The results are explained with 2D and 3D visualizations.
I propose several models to understand how people view the relationships between work and life: (1) life = resting life, (2) life = work life + nonwork life, and (3) life > work life + nonwork life.