In this second Open Risk White Paper on "Connecting the Dots" we examine measures of concentration, diversity, inequality and sparsity in the context of economic systems represented as network (graph) structures.

Concentration, diversity, inequality and sparsity in the context of economic networks In this second Open Risk White Paper on Connecting the Dots we examine measures of concentration, diversity, inequality and sparsity in the context of economic systems represented as network (graph) structures. We adopt a stylized description of economies as property graphs and illustrate how relevant concepts can represented in this language. We explore in some detail data types representing economic network data and their statistical nature which is critical in their use in concentration analysis.

We explore a variety of distinct uses of graph structures in data science. We review various important graph types and sketch their linkages and relationships. The review provides an operational guide towards a better overall understanding of those powerful tools

Graphs seem to be everywhere in modern data science: Graphs (and the related concept of Networks) have emerged from a relative mathematical and physics niche to an ubiquitous model for describing and interpreting various phenomena. While the scholarly account of how this came about would probably need a dedicated book, there is no doubt that one of the key factors that increased the visibility of the graph concept is the near universal adoption of digital social networks.

What this blog post is about (and what it isn’t): With the ever more widespread adoption of Data Science, defined as the intensive use of data in various forms of decision making, there is a renewed interest in Visualization as an effective channel for humans to understand data at various stages of the data lifecycle.
There is a large variety of data visualization tools which can produce an ever more bewildering variety of visualization types

Data Quality and Exploratory Data Analysis using Python: In two new Open Risk Academy courses we figure step by step how to use python to work to review risk data from a data quality perspective and how to perform exploratory data analysis with pandas, seaborn and statsmodels:
Introduction to Risk Data Review Exploratory Data Analysis using Pandas, Seaborn and Statsmodels

The RegDB Explorer is a web app that helps you get the big picture when it comes to regulatory documentation. In a simple visual setting you can see the time sequence of publications, the topic and number of pages and select/click-through to any document published by the Basel Committee on Banking Supervision.(The app data are derived from the published BIS repositories with minimal adjustments)
You can find the RegDB explorer here.