The Value of a Professional Network?
An open letter to LinkedIn’s data scientists.
In “The Startup of You”, LinkedIn co-founder Reid Hoffman advises to “strengthen your professional network by building powerful alliances and maintaining a diverse mix of relationships” and to “tap your network for information and intelligence that help you make smarter decisions.” It’s great advice — a 21st-century update of “it’s not what you know, it’s who you know.”
But I’m not aware of any scientific analysis that establishes the return on investment for developing your professional network. If you’re an outbound professional — such as a salesperson or recruiter — then you’ve learned from experience that having a broader network helps you do your job. For the rest of us, the value of a professional network may not be quite as obvious.
While I worked at LinkedIn, I advocated for data scientists to try to measure the value of a professional network, especially as part of LinkedIn’s work on the Economic Graph. I’m not aware of any scholarship in this area — from LinkedIn or anyone else — and I feel it’s an area ripe for research.
Analyzing the value of a professional network starts with modeling the inputs and outputs, i.e., deciding how to measure network strength and professional utility. But it’s not hard to come up with models for both of these. Personal networks have many measurable attributes that reflect size, reach, diversity, and connection strength. Similarly, there are many ways to measure professional utility: income, speed of career advancement, attention from other professionals, etc. The precise choices of models aren’t that important, given the correlations within each set of measures.
A trickier issue will be establishing causality. It’s difficult to determine whether a person’s professional network has contributed to professional success, or vice versa. There has been some work on establishing causality in social networks, but identifying the exogenous variables will require some care.
Still, the biggest challenge for this research is access to data. LinkedIn and Facebook are probably the only organizations that have the data necessary for this research, and neither company makes its data available to the general public because of their concerns with protecting users’ privacy and preserving their own competitive advantage. Their own data scientists could investigate this topic, but I recognize that there are always more interesting topics to investigate than there are data scientists to investigate them. Prioritization is ruthless.
Nonetheless, I feel this topic is an existential one for LinkedIn, and I hope someone there will find the time to investigate it. Or that the company will pursue this area through collaboration with academic researchers, perhaps through a future iteration of the Economic Graph Challenge.
I strongly believe that professional networks help us both individually and collectively. But as a scientist, I’d rather rely on evidence than on faith. I hope that those of you with access to the data will help produce that evidence.