Issue #209

July 13, 2023

This week’s Data Science Book, "Network Science with Python", by D. Knickerbocker, is a highly recommended book for anyone interested in network analysis. It provides a comprehensive and accessible introduction to the topic. The book's linear progression and friendly tone make it highly engaging and easy to follow. The author's contagious enthusiasm and practical examples effectively communicate the power and importance of network analysis. The book covers various domains, including language and social media data mining, and explores the relationship between NLP and networks, an approach similar to our very own Graphs for Data Science substack. It emphasizes the value of actionable insights in the conversational AI domain and provides historical context and real-world use cases for NLP solutions. The book also introduces the Python packages used and dives into network science using the NetworkX library. It demonstrates how graphs can be used in machine learning and covers important concepts like betweenness centrality, page rank, and community detection with real-world applications. Overall, "Network Science with Python" is a well-written and comprehensive guide that offers practical insights and is suitable for readers of all levels.

Network Science with Python

1. Announcing the first Machine Unlearning Challenge [ai.googleblog.com]
2. Improve ChatGPT with Knowledge Graphs [mlabonne.github.io]
3. A.I. Is Coming for Mathematics, Too [nytimes.com]
4. System Design Course [github.com/karanpratapsingh]
5. How Coders Can Survive—and Thrive—in a ChatGPT World [spectrum.ieee.org]
6. Advanced Techniques for Prompt Engineering [medium.com/@alcarazanthony1]
7. The Weirdest Result in Mathematics [cantorsparadise.com]

• Long ties, disruptive life events, and economic prosperity (E. Jahani, S. P. Fraiberger, M. Bailey, D. Eckles)
• Future directions in human mobility science (L. Pappalardo, E. Manley, V. Sekara, L. Alessandretti)
• A panel dataset of COVID-19 vaccination policies in 185 countries (E. Cameron-Blake, H. Tatlow, B. Andretti, T. Boby, K. Green, T. Hale, A. Petherick, T. Phillips, A. Pott, A. Wade, H. Zha)
• Leveraging WiFi network logs to infer student collocation and its relationship with academic performance (V. Das Swain, H. Kwon, S. Sargolzaei, B. Saket, M. Bin Morshed, K. Tran, D. Patel, Y. Tian, J. Philipose, Y. Cui, T. Plötz, M. De Choudhury, G. D. Abowd)
• LongNet: Scaling Transformers to 1,000,000,000 Tokens (J. Ding, S. Ma, L. Dong, X. Zhang, S. Huang, W. Wang, F. Wei)
• Post-COVID Inflation & the Monetary Policy Dilemma: An Agent-Based Scenario Analysis (M. S. Knicker, K. Naumann-Woleske, J.-P. Bouchaud, F. Zamponi)
• Neuronal synchronization in time-varying higher-order networks (M. S. Anwar, D. Ghosh)

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July 13, 2023

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