Besides social networks in our daily lives such as Facebook or Twitter, networks now play an increasingly important role in both academic research and industry. Network analysis has become an indispensable toolkit as more and more relational data are made available. In this video, F. Bill Shi of Knowledge Lab reviews the state of the art of modern network analysis, and provides a hands-on tutorial on three problems that have found successful applications in the real world (one of which has made billions of dollars): how to rank nodes, how to identify meaningful clusters of nodes, and how to predict potential links in a network.
To follow along with this workshop, download the iPython notebook and datasets at: github.com/KnowledgeLab/CI-Workshop-Networks
Python 2.7, igraph ( igraph.org/python/#startpy), and gephi ( gephi.github.io/) are also recommended for this tutorial.
Discovery Engines: Under The Hood
Recent years have brought an explosion of software and tools for working with data, making once arduous tasks such as processing, analysis, machine learning, and visualization easy and accessible. But as the data science toolbox grows larger and larger, it becomes harder to find the proper tool for the job at hand, not to mention how to get the most out of its use. "Discovery Engines: Under the Hood" is a new monthly workshop series organized by the Computation Institute, offering practical, hands-on instruction with new and popular computational tools. Learn from experienced users at CI research centers about programming libraries, web platforms, visualization software, and analytic techniques that can make a difference in your own projects.
- Discovery Engines: Network Analysis ( Download)
- Building a Discovery Engine with Machine Learning: AI With The Best Oct 2017 ( Download)
- Discovery Engines: Climate Data & Tools Part 1 ( Download)
- Network Medicine, Systems Pharmacology, and Cardiovascular Drug Discovery ( Download)
- Lightning Talk 1: A Python discovery engine ( Download)
- Building a Discovery Engine with Machine Learning by Gary Sieling ( Download)
- Unsupervised Network Discovery for Brain Imaging Data ( Download)
- Discovery Engines: Statistical Learning with Python and pandas ( Download)
- Machine learning powered metabolomic network analysis ( Download)
- Why SnapStart Discovery Engine Here’s Why. ( Download)
- CEH Module 3 Lab 3: Perform OS Discovery ( Download)
- 31- Firepower Network Discovery Policy ( Download)
- 20. Cisco FTD Network Discovery Policy ( Download)
- In Depth Network Discovery Made Easy Using RunZero @runZeroInc ( Download)
- THOORA - Content Discovery Engine ( Download)