University Rankings use a variety of factors to rank universities. It reflects how universities compare with each other in different areas, e.g. starting salaries, employment rates, research production, etc. University Rankings have been shown to be a significant factor that affects not only the number but also the quality of applicants.
Recently more sophisticated metrics beyond simple tabular data are starting to be utilized in the evaluation and rankings of universities. Alumni outcomes is posed to become an important component of university ranking systems in the future. Any metrics around this would have to provide a quantitative proxy for categories such as:alumni network value and alumni influence. Both of which are closely intertwined and could be estimated using the network centric approach.
Alumni network is an abstract concept that usually refers to a network of social and business connections among the alumni. Alumni Network increases the value and importance of building relationships with alumni, students, staff and faculty; and benefits of that are multi faceted and mutually beneficial.
While alumni network metrics are very important. None of the traditional metrics; capture well properties of the networks. The Traditional metrics of the bellow universities such as salary and employment rate are very similar; yet they differ significantly if we examine them from the network perspective. In the network bellow node colors represent different entities; purple is university, blue is alumni, red is company. Size of the node reflects node’s degree (log-scaled).
I hope that so far our proposal to evaluate alumni networks as networks sounds reasonable. So why hasn’t it been done?For the most part it 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 employees 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 significant 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. 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 relations/links that interconnect these entities. Overall we have acquired data on 2,100 educational institutions and 5,800 personal educational affiliations.
Intra-University Networks. Networks of the above universities are expanded in a breadth first manner up to the depth of 2, (showing university, alumni and companies they are associated with through employment, investment or other activities). Intra-university networks were produced for Stanford University, Harvard University, University of California (Berkeley), and MIT. Some differences are immediately apparent across the intra-university networks. The number of company nodes in relation to number of alumni nodes differs. Stanford University has a significantly higher ratio of companies per alumni in leadership roles, followed by Harvard, trailed by Berkeley, and MIT. A high ratio of company nodes indicates that alumni have been involved with multiple companies - either through employment, advisory or investment activities. A particular characteristic of highly connected alumni (large nodes with many connections located on the perimeter) stands out, namely their collaboration patterns. Stanford's densely connected alumni are highly likely to cross-collaborate multiple times with fellow alumni (indicated by the company nodes being pulled away from highly connected alumnus towards other less-connected alumni in the center), and multiple intersecting connections. In the networks of other universities, the collaboration of highly connected individuals with their fellow alumni is evidenced but to a lesser degree.
Inter-University Network (between Stanford, Harvard, MIT, Berkeley). Network is obtained by starting with the nodes of the above mentioned universities and performing a breadth-first expansion up to the depth of 3.Characteristics:Two distinct groups:universities (in the lower left corner) financials (in the upper right corner) – are visible in the subdued edges in The distance from the universities to the cloud of `financial' clusters also varies. In particular, Stanford and Berkeley are rather close to the financial cloud. This may be explained by the geographical proximity of these universities to one of the largest sources of venture funding -- Silicon Valley. While universities themselves are not embedded within the financial clusters, a noticeable proportion of alumni are deeply connected within the financial clusters by having direct or indirect relations with multiple financial organizations. Stanford has the largest number of alumni connected to the financial cluster, followed by Harvard (even though university itself is relatively distant from the financial cluster); followed by Berkeley, and only a few alumni from MIT. The proximity between alumni and their alma matters appear to differ significantly. Berkeley alumni tend to be clustered together, MIT to somewhat lesser degree, and Stanford and Harvard alumni are rather dispersed. Proximity between universities differs as well. Stanford and Berkeley are close together (many alumni hold leadership positions in the same companies). One of the likely explanations for this network proximity is the geographical proximity of both universities to Silicon Valley where many of the investment firms and startup companies are located. Harvard and MIT do not appear to have as strong relations with other universities in these settings.
Universities within the Business Network (partial snapshot) Note that network patterns differ significantly from Inter-University Network; due to additional forces exerted by a very large number of nodes and links of the complete network (144,685 nodes and 129,423 links). For better visibility of the entity types except for universities are faded out.Let us look at the proximity between universities and companies. While Microsoft and Yahoo are close to many major universities, Google appears to be distant from them; our hypothesis is that this is caused by Google experiencing very rapid employee growth, which has required establishing relationships with many universities to meet hiring goals.While engineers play a key role, they often do so in a technology development capacity rather than in the leadership positions that are visible in public relations communications. Some support for this explanation may be seen in the relatively large distance between Microsoft and University of Washington, even though a large number of engineers at Microsoft are indeed from University of Washington.
We can look at the potential network effects of university node; by comparing networks that include university node with the ones that don’t. By adding the university node, connecting it to the alumni, and comparing how this effects metrics, we can see the potential network effect of a university and its alumni. In this case we quantify how well connected alumni are, by utilizing the betweeness centrality metric.First we look at the network in which university node is not present (on the left). It shows that the well-connected individuals are well connected even without the presence of university node; however connectedness drops off exponentially; and most interestingly become similar across universities. On the right side the network that includes university node is shown. Interestingly the ranking of highest connected individuals does not change much (since they are already well connected); however by playing role of connector university can significantly increase connectedness of the rest of the alumni. Connectedness potential of different universities differs (upper bound). However, if university actively participates in connecting its alumni it can surpass universities that have higher network potential but did not utilize it. By university interconnecting the alumni university improves not only its own connectedness by also of its alumni.
Let us take a closer look at the metrics of alumni networks with and without the university node. Let us comment on some apparent characteristics:First observation, is that developed university network is a great equalizer for many of the metrics such as closeness centrality, eccentricity; that is without the university node these metrics of universities differ a lot; e.g. median eccentricity of Oxford alumni is 3; while it is 20 of Columbia university. However once the university node is added median eccentricity of both universities becomes equal to 19.Finally, the degree to which university connects it alumni significantly affects the properties of network.
I would like to conclude with two statements:First, Alumni Networks are networks, so should be analyzed as such.Second, Developing alumni networks can have significant positive impactfor both alumni and universities.
This paper is produced by the Innovation Ecosystems Network founded at Media X, Stanford University. The goal of this interdisciplinary group is investigate how networks (in a broad sense) could be used and developed in a variety of areas including innovation, business, finance, journalism and education. In this presentation, we concentrate on alumni networks.
Alumni Network Analysis
Alumni Network Analysis<br />Innovation Ecosystem Network, Media X, Stanford University<br />N. Rubens, M. G. Russell, R. Perez, J. Huhtamaki, K. Still, D. Kaplan, and T. Okamoto, “Alumni Network Analysis,” in Global Engineering Education Conference (EDUCON), 2011 IEEE, Amman, Jordan, 2011, pp. 606-611. <br />
University Rankings<br />Lists of institutions in higher education, ordered by combinations of factors.<br />Significantly affects colleges' applications and admissions (Bowman & Bastedo, 2010).<br />
University Rankings & Alumni Networks<br /> Alumni outcomes are a likely (if not inevitable) component of university ranking systems in the future. Any metrics around this would have to provide a quantitative (or rank ordered) proxy for categories such as<br />alumni network value <br />alumni influence<br />(Dan Guhr, Illuminate Consulting Group)<br />
Alumni Networks<br />A network of social and business connections among the alumni (wiki).<br />Increases the value and importance of building relationships with alumni, students, staff and faculty (Haas, Berkeley). <br />http://www.intelalumni.org/about/<br />
Limitations of Traditional Rankings<br />Traditional rankings do not capture well the ‘network’ properties of alumni networks.<br />
Challenges<br />Need cross institutional, cross company data.<br />This data is rarely shared.<br />IEN Dataset (based on socially constructed data)<br />Updated quarterly with rapid growth each quarter<br />2,100 educational institutions <br />5,800 personal educational affiliations<br />focuses on people in leadership/entrepreneurial roles<br />Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” <br />Technical Report. Media X, Stanford University, Feb.2010.<br />Martha <br />
Intra University Networks<br />Characteristics<br />entrepreneurship<br /># companies / # alumni<br />collaboration patterns<br />(cross vs singular)<br />network flow<br />
Inter-University Network<br />Characteristics<br />Clustering<br />universities (in the lower left corner) <br />financials (in the upper right corner) <br />Distance:<br />between universities<br />between university and financials<br />between university and alumni<br />
Comparison<br />w/o University Node<br />w/ University Node<br />Characteristics<br />“well connected” alumni are well connected with or without the university node<br />University can significantly impact connectedness of less connected alumni<br />University with good network potential can differentiate itself from other universities.<br />
w/o University Node<br />w/ University Node<br />Characteristics<br />* Developed university network is a great equalizer: closeness centrality, eccentricity<br />* The degree to which university develops its network can change its characteristics.<br />
Conclusion<br />Alumni Networks are networks, so should be analyzed as such.<br />Developing alumni networks can have significant positive impact<br />for both alumni and universities.<br />
References<br />N. Rubens, M. G. Russell, R. Perez, J. Huhtamaki, K. Still, D. Kaplan, and T. Okamoto, “Alumni Network Analysis,” in Global Engineering Education Conference (EDUCON), 2011 IEEE, Amman, Jordan, 2011, pp. 606-611. <br />For more information see: <br />http://www.innovation-ecosystems.org/2010/12/01/alumni-networks/<br />http://activeintelligence.org/blog/archive/alumni-network-analysis/<br />Network Visualization: Gephi<br />Network Analysis: Gephi , JUNG, NetworkX<br />
The Innovation Ecosystems Network (IEN) brings together an international interdisciplinary team that seeks to develop and diffuse novel data and tools for understanding the catalytic impact of regional ICT experiments.<br />http://www.innovation-ecosystems.org<br />