Innovation Ecosystem Network, Media X, Stanford University

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.

Alumni Network Analysis

University Rankings

• Lists of institutions in higher education, ordered by combinations of factors.

• Significantly affects colleges' applications and admissions (Bowman & Bastedo, 2010).

University Rankings & Alumni Networks

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

• alumni network value • alumni influence

(Dan Guhr, Illuminate Consulting Group)

Alumni Networks

• A network of social and business connections among the alumni (wiki).

• Increases the value and importance of building relationships with alumni, students, staff and faculty (Haas, Berkeley).

http://www.intelalumni.org/about/

Limitations of Traditional Rankings• Traditional rankings do not capture well the

‘network’ properties of alumni networks.

IEN Dataset (based on socially constructed data)

Martha Neil Rubens, Kaisa Still, Jukka Huhtamaki, Martha G. Russell “Leveraging Social Media for Analysis of Innovation Players and Their Moves” Technical Report. Media X, Stanford University, Feb.2010.

Updated quarterly with rapid growth each quarter

2,100 educational institutions 5,800 personal educational affiliationsfocuses on people in leadership/entrepreneurial roles

Challenges• Need cross institutional, cross company data.• This data is rarely shared.

Intra University Networks

Characteristics

entrepreneurship# companies / # alumni

collaboration patterns(cross vs singular)network flow

Inter-University NetworkCharacteristics

Clusteringuniversities (in the lower left corner) financials (in the upper right corner)

Distance:between universitiesbetween university and financialsbetween university and alumni

University-Company Network

Characteristics

Different from Inter-University Network

Preferential attachment / proximitybetween Universities-Companies

w/o University Node w/ University Node

Characteristics“well connected” alumni are well connected with or without the university nodeUniversity can significantly impact connectedness of less connected alumniUniversity with good network potential can differentiate itself from other universities.

Comparison

w/o University Node w/ University NodeCharacteristics* Developed university network is a great equalizer: closeness centrality, eccentricity* The degree to which university develops its network can change its characteristics.

Conclusion

Alumni Networks are networks, so should be analyzed as such.

Developing alumni networks can have significant positive impactfor both alumni and universities.

ReferencesN. 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.

For more information see: http://www.innovation-ecosystems.org/2010/12/01/alumni-networks/http://activeintelligence.org/blog/archive/alumni-network-analysis/

Network Visualization: Gephi

Network Analysis: Gephi , JUNG, NetworkX

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.

http://www.innovation-ecosystems.org