It was a widely observed heuristic going back to the days when the Semantic Web was trendy. The underlying reason is also obvious once stated.
Almost every non-trivial graph data model about the world is a graph of human relationships in the population. If not directly then by proxy. Population scale human relationship graphs commonly pencil out at roughly 1T edges, a function of the population size. It is also typically the highest cardinality entity. Even the purpose isn’t a human relationship graph, they all tend to have one tacitly embedded with the scale implied.
If you restrict the set of human entities, you either end up with big holes in the graph or it is a graph that is not generally interesting (like one limited to company employees).
The OP was talking about generalizing this to a graph of people, places, events, and organizations, which always has this property.
It is similar to the phenomenon that a vast number of seemingly unrelated statistics are almost perfectly correlated with GDP.
Almost every non-trivial graph data model about the world is a graph of human relationships in the population. If not directly then by proxy. Population scale human relationship graphs commonly pencil out at roughly 1T edges, a function of the population size. It is also typically the highest cardinality entity. Even the purpose isn’t a human relationship graph, they all tend to have one tacitly embedded with the scale implied.
If you restrict the set of human entities, you either end up with big holes in the graph or it is a graph that is not generally interesting (like one limited to company employees).
The OP was talking about generalizing this to a graph of people, places, events, and organizations, which always has this property.
It is similar to the phenomenon that a vast number of seemingly unrelated statistics are almost perfectly correlated with GDP.