for - search - google - high resolution addressing of disaggregated text corpus mapped to graph - search results of interest - high resolution addressing of disaggregated text corpus mapped to graph

search - google - high resolution addressing of disaggregated text corpus mapped to graph - https://www.google.com/search?q=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&oq=high+resolution+addressing+of+disaggregated+text+corpus+mapped+to+graph&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRigATIHCAIQIRigAdIBCTMzNjEzajBqN6gCALACAA&sourceid=chrome&ie=UTF-8

to - search results of interest - high resolution addressing of disaggregated text corpus mapped to graph - A New Method for Graph-Based Representation of Text in - The use of a new text representation method to predict book categories based on the analysis of its content resulted in accuracy, precision, recall and an F1- ... - https://hyp.is/H9UAbk46Ee-PT_vokcnTqA/www.mdpi.com/2076-3417/10/12/4081 - Encoding Text Information with Graph Convolutional Networks - According to our understanding, this is the first personality recognition study to model the entire user text information corpus as a heterogeneous graph and ... - https://hyp.is/H9UAbk46Ee-PT_vokcnTqA/www.mdpi.com/2076-3417/10/12/4081

Lima, Manuel. The Power of Networks. RSA ANIMATE, 2012. https://www.youtube.com/watch?v=nJmGrNdJ5Gw.

https://www.youtube.com/watch?v=z3Tvjf0buc8

graph thinking

• intuitive
• speed, agility
• adaptability

; graph thinking : focuses on relationships to turn data into information and uses patterns to find meaning

property graph data model

• relationships (connectors with verbs which can have properties)
• nodes (have names and can have properties)

Examples:

• Purchase recommendations for products in real time
• Fraud detection

Use for dependency analysis

Singh, K., Lima, G., Cha, M., Cha, C., Kulshrestha, J., Ahn, Y.-Y., & Varol, O. (2021). Misinformation, Believability, and Vaccine Acceptance Over 40 Countries: Takeaways From the Initial Phase of The COVID-19 Infodemic. ArXiv:2104.10864 [Cs]. http://arxiv.org/abs/2104.10864

Plata, C. A., Pigani, E., Azaele, S., Callejas, V., Palazzi, M. J., Solé-Ribalta, A., Meloni, S., & Suweis, J. B.-H. S. (2020). Neutral Theory for competing attention in social networks. ArXiv:2006.07586 [Physics]. http://arxiv.org/abs/2006.07586

Jazayeri, A., & Yang, C. C. (2020). Motif Discovery Algorithms in Static and Temporal Networks: A Survey. ArXiv:2005.09721 [Physics]. http://arxiv.org/abs/2005.09721

Costantini, S., De Meo, P., Giorgianni, A., Migliorato, V., Provetti, A., & Salvia, F. (2020). Exploring Low-degree Nodes First Accelerates Network Exploration. ArXiv:2005.08050 [Physics]. http://arxiv.org/abs/2005.08050

COMBINE Network Biology at UMD. (2020 April 09). Net-COVID Session1A: Network epidemiology tutorial by Laurent Herbert-Dufresne Youtube. https://www.youtube.com/watch?v=5NGFDnJKiKA

Netheads vs Bellheads

Seems like this is a critical assumption to examine for current media literacy/misinformation discussions. As networks become very large and very flat, does this assumption of reciprocity and good faith hold? (I'm thinking, here, of people whose expertise I trust in one domain but perhaps not in another, or the fact that sometimes I'm talking to one part of my network and not really "actively seeking information" for other parts.)