1 Alumni Network Analysis Neil Rubens 1,2 , Martha Russell 1,3 , Rafael Perez 2 , Jukka Huhtamäki 1,4 , Kaisa Still 1,5 , Dain Kaplan 6 Abstract—Alumni connections are important resources that contribute to university evaluation. Even though alumni con- nections represent networks, they have been mostly evaluated as tabular data (e.g. by providing average salary, employment rate, etc.). This ironically disregards all qualities of a network, from which an alumni network gets its name. It is desirable to evaluate an alumni network as a network, because networks have the potential to provide very insightful information. Evaluation of alumni networks as a network has not been feasible in the past due to data fragmentation (neither universities nor companies willing to share meaningfully significant data in its entirety). Recently the feasibility of such an analysis has changed, due to new trends towards democratization of information, accelerated by the Web 2.0 user-generated content phenomenon and crowd- sourcing mentality. Utilizing web-crawlers, we actively harvested data and assembled a dataset on alumni in leadership positions in technology-based industries. Moreover, we include a high proportion of startup companies, which allowed us to evaluate alumni networks with respect to entrepreneurial as well as technology involvement. We show that by analyzing alumni connections as networks, it is possible to uncover new patterns, as well as provide a new way of examining the old. Index Terms—alumni networks, university metrics, network analysis, network visualization, entrepreneurship, engineering I. I NTRODUCTION Rankings of universities and their programs abound. Some rankings are based on numerical scores; some are based on expert judgment. Generally, the rankings are endorsed by those at the top and held in suspect by some of the others. The factors that contribute to the preeminence of educational insti- tutions are complex, and controversies surround nearly every ranking. The top-ranked institutions leverage rankings in their recruitment of faculty and students, in their appeals to donors, in outreach to prospective employers of their graduates, and in their requests for program and research funding. Around the world, national innovation policy groups use measures of alumni impact in their analysis and policy recommendations. Data on alumni has been used to estimate quality and impact of educational institutions. Tabular data about individuals’ starting salaries, employment rate, and donation have been used to determine averages and comparisons. Some analyses refer conceptually to the network of an institution’s alumni, even though the analyses disregard the relationships qualities of a network, from which an alumni network gets its name. Our objective, therefore, is to provide a much overdue evalu- ation of the relationship characteristics of alumni networks. http://innovation-ecosystems.org/alumni-network 1 Innovation Ecosystems Network, Media X, Stanford University, USA 2 University of Electro-Communications, Japan 3 HSTAR Institute, Media X, Stanford University, USA 4 Tampere University of Technology, Finland 5 VTT Technical Research Centre, Finland 6 Tokyo Institute of Technology, Japan We take two complementary approaches for this analysis: (1) visual: by providing a visualization of the network for a comprehensive and explorative view; and (2) numerical: by providing metrics that capture salient features of the network. In defense of traditional approaches, the network analysis of alumni has been hindered by lack of data suitable for network analysis. Gathering data about alumni is time-intensive; and the limited data available on alumni is considered a precious resource and is closely guarded by universities. To exacerbate the problem, the release of corporate information about em- ployees and their education is limited, as well. The available data lacks standardized units of measure, is disjointed, and is problematic for analysis. It is therefore no wonder that universities have used their data in a very limited manner, namely, self-benchmarking or in support of the fund-raising efforts of their development offices. Due to recent trends towards democratization of knowledge and information, accelerated by the Web 2.0 user-generated content phenomenon and crowd-sourcing mentality, a signifi- cant amount of data on alumni is becoming available, though still scattered throughout the web. Utilizing web-crawlers, we actively harvested data and assembled a dataset on alumni in executive, investor and board level positions in technology- based industries (including many startup companies) and the service sectors that support them [1] (Section II-C). This combination provides information about both entrepreneurial and technological involvement of alumni. In order to capture network properties, we have collected not only data about the direct university alumni connections, but also data about their employment histories, company information, financial organizations, investment activities, and most importantly re- lations/links that interconnect these entities. We propose a novel approach to evaluate the connectivity of alumni based on their leadership roles in technology- based businesses. As the available data increases, the proposed approach can be more widely applied to gain broader and deeper insights into alumni networks. The goal of this paper is to demonstrate ways in which this could be accomplished. For example, we investigate the role an alumni network plays in enhancing a personal network (Section III-B), comparing the alumni networks of different universities (Section III-A), etc. The rest of this paper focuses on providing a brief overview of these possibilities. II. METHOD A. Conceptual Approach Our approach is based on a network analysis of alumni in leadership roles in technology-based companies, the service agencies that support them, and their investment firms. The