Update week 2022-04-04:

• implemented code to get different knowledge networks from reading sessions https://gitlab.wikimedia.org/repos/research/curiosity/-/blob/main/example_session2network_clean.ipynb
• started to look into network-libraries networkx and graph-tool to calculate network-metrics

Update week 2022-04-11:

• Started to implement network features for different networks:
• Clustering, Characteristic Path Length, Small-world propensity, and core-periphery structure (code). This requires some iterations to adapt to the specific use-case (e.g. weighted networks)
• Extract meso-scale structure from fitting blockmodels with graph-tool. This allows us to quantify modularity and compression of the network (code)

Update week 2022-04-18:

• continuing to implement different network-metrics, including the comparison with random null models.

Update week 2022-04-25:

• Generated a single pipeline for a set of 9 different metrics to characterize the knowledge networks using different packages https://gitlab.wikimedia.org/repos/research/curiosity/-/blob/main/data_knowledge-networks_metrics-combined_clean.ipynb
• Generated a smaller and more homogenous subsample of the original dataset of reading sessions (N=1000, only English Wikipedia, only pagerequests from single country) to facilitate exploratory analysis https://gitlab.wikimedia.org/repos/research/curiosity/-/blob/main/example_session2network_clean.ipynb
• Next step is to run the metrics for the 1000 reading sessions and compare with KNOT-data used in previous studies on curiosity of Wikipedia readers in https://www.nature.com/articles/s41562-020-00985-7 and https://arxiv.org/abs/2204.01182

Update week 2022-05-02:

• extracted metrics of knowledge networks for subsample https://gitlab.wikimedia.org/repos/research/curiosity/-/blob/main/analyze_1000-networks.ipynb
• next step: compare networks with results from previous studies using KNOT-data

Update week 2022-05-09:

• looking at descriptive statistics of network metrics and comparing with statistics from KNOT-data

Update week 2022-05-16:

• evaluating descriptive network statistics of knowledge networks from Wikipedia readers and from KNOT-data https://gitlab.wikimedia.org/repos/research/curiosity/-/blob/main/analyze_1000-networks_comparison-knot-rw_clean.ipynb
• the distributions of clustering, path length, core-ness, modularity are very similar among the two different populations
• this behaviour cannot be captured by null models based on random exploration of the Wikipedia network. this means that the knowledge networks capture structure in the navigation of readers beyond the observations of "Wikipedia Reader Navigation: When Synthetic Data Is Enough" (in which synthetic data generated from random walks closely approximated some observations of reader navigation)

Update week 2022-05-30:

• given the similarity in the knowledge networks of Wikipedia readers and KNOT-study, we have been trying to reproduce the findings that there are 2 main types of readers characterized by hunter- and busybody-style of curiosity.

Update week 2022-06-06:

• continuing analysis of knowledge networks of readers. main principal component seems to be associated with hunter and busybody types described in Lydon-Staley etal. Performing robustness checks of the results by calculating additional network metrics and comparing with Random Walk models.

Update week 2022-06-13:

• added dataset of knowledge networks from targeted navigation (wikispeedia-data) for comparison. The corresponding networks are substantially different than navigation of Wikipedia readers (specifically in terms of shorter characteristic path lengths)
• compiled different results from the first exploratory analysis. We will prepare a summary and started to discuss next steps for the analysis.

Update week 2022-06-20:

• debugging the calculation of some metrics for the knowledge networks
• summarizing results and discussing next steps

Update week 2022-06-27:

• writing up results of first round of exploratory analysis for meta

Update week 2022-07-18:

• finishing write-up for meta about the first round of exploratory analysis (planning to put on meta next week)
• sketched first draft for the structure of the paper