AngelList Funding

Identifying key investors in Angellist Funding.

Angellist exposes a well documented api to get information about the funding for each companies, their angellist investors and many other information about the venture capital funding on angellist. Lets look at the angellist company funding.

Getting Data.

  • Find the company investors.
  • Create a graph of follow/follower information between investors.



This measure tells us which people are most “between” other people. We can say that a person who is on the shortest path of connections between other people is between them. Another way of putting this is that if there is a set of connections between A and Z going through other people, then if Q is on a path which is the shortest path between A and Z then Q is said to be between A and Z.
A person who is between a lot of other people has a higher betweenness centrality measure than a person who is not between many other people. Betweenness is useful because it potentially tells us which people are the key connectors of other people, or groups of people. In our example Thomas Korte thomask has the most betweenness centrality.

Authority and PageRank.


In this graph created we can see that Authority and PageRank for Mitch Kapoor to be high mkapor. PageRank and HITS are pioneering approaches that introduced link analysis ranking, in which they use the links pointing in to a node to define the important nodes.
So its quite possible that the investment of Mitch Kapor in Angellist could have made other investors to invest in Angellist funding. If at all angellist had provided an api for finding out when a investor invested in the company we could have analysed if those investments were influenced by the the authoritative node(Mitch Kapoor).

Eigenvector Centrality.


I found this definition of Eigenvector centrality in IPL intelligence business.
Eigenvector centrality is a little bit harder to describe easily, but is one of the most powerful techniques in the social network analysis toolkit. This measure takes into account not just the number of links that each person has (as in degree centrality), but also the number of links of the connected people, and their links too, and so on throughout the network. So if A is the key player in the group, with lots of connections to many other people, then a person B connected directly to A (but only to A) still has a lot of importance, even though B has only one connection. Person Z, out at the edge of the network might be connected to three people, but if those individuals are not of high importance themselves, then Z’s importance is similarly low. If we rank people by eigenvector centrality, we can see who the key important people are in the network. At the top of the list these may be obvious, but things can get more interesting as we see people who have a high eigenvector centrality even though they are not obviously important. Their appearance high up in the list gives us a clue that we may need to investigate further to determine why they are so high.

Finding the missing link.

The graphs clearly show that there exists some follow/follower relation between investors, but there was one outlier in our case Daniel Gould. It was surprising to see someone invest in a company who doesn’t have any link with other investors. After exploring more found out that Dave morin who is currently advising AngelList is connected to Daniel Gould.

About the author


Backend/Infrastructure Engineer by Day. iOS Developer for the rest of the time.

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