In graph theory, a graph (network) is a structure containing a set of objects in which the objects are in some sense "related". The objects can be ideas, concepts, discrete elements. I work with them in medicine and biology to analyze relationships between proteins, genes, diseases etc. I am applying similar principles to do deep dives of companies to find great companies to invest in.
Each node is a tangible concept or entity. Lines connecting the nodes are relationships between any two entities. Thus, if 2 entities are related, there is a line connecting 2 nodes. This type graph is called a network and is an excellent tool to represent relationships between 'things'. These graphs are a mathematical model of relationships. Mathematical algorithms calculate the specific position of the nodes. This calculation dependent on relationships between the nodes. Thus, this is an unsupervised graph, meaning, the placement of the nodes is by mathematical modeling as opposed to human bias. The information in the relationships decides which node is placed where.
For example: Mercadolibre (Company) - (is connected to) - Mercado Pago - (is connected to) - credit service.This can be represented as a simple graph "Mercadolibre - MercadoPago - Credit service".
This method has additional useful applications. One, it can be used to analyze governance structure of companies and the various experiences the leadership brings to the table. Another one, it to apply this to earnings conference call information to see if there is a positive or negative factor affecting the network. Such visual tools can help increase understanding and reading between the lines.
In my experience, this type of analysis is crucial but severely lacking since most analysts chose either fundamental (profit, loss, revenue etc) or technical analysis (stock price action). The reason this is so important in my opinion, is to understand the underlying business and to
get a sense of scale, interconnections, shared strategic resources and inter-dependencies.
Connections can make a company strong or can drag it down to oblivion if
it does not have positive synergies.
An just for fun, here is the story of Goldilocks and 3 bears depicted as a unsupervised semantic network graph.
