Predicting success based on social network analysis
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1 Predicting success based on social network analysis Ingo Scholtes in collaboration with F Schweitzer, MS Zanetti, E Sarigöl, R Pfitzner, CJ Tessone, A Garas ETH Zürich Quantifying 13, Barcelona Ingo Scholtes ETH Zürich Data-driven modeling of socio-technical systems Quantifying 13, Barcelona Ingo Scholtes
2 Quantifying 13, Barcelona Ingo Scholtes Firefox Thunderbird Eclipse NetBeans Total Start date April 2002 January 2000 October 2001 January 1999 Bug reports 112,968 35, , , ,692 Changes 1,068, ,957 2,594,385 1,875,878 5,852,290 Valid 21 % 24 % 74 % 63 % 57 % Faulty 79 % 76 % 26 % 37 % 43 % Firefox: 79 % of all bug reports are not valid Quantifying 13, Barcelona Ingo Scholtes
3 Firefox Thunderbird Eclipse NetBeans Total Start date April 2002 January 2000 October 2001 January 1999 Bug reports 112,968 35, , , ,692 Changes 1,068, ,957 2,594,385 1,875,878 5,852,290 Valid 21 % 24 % 74 % 63 % 57 % Faulty 79 % 76 % 26 % 37 % 43 % How to identify a valid bug report at the time of submission? Quantifying 13, Barcelona Ingo Scholtes Firefox Thunderbird Eclipse NetBeans Total Start date April 2002 January 2000 October 2001 January 1999 Bug reports 112,968 35, , , ,692 Changes 1,068, ,957 2,594,385 1,875,878 5,852,290 Valid 21 % 24 % 74 % 63 % 57 % Faulty 79 % 76 % 26 % 37 % 43 % How to predict success? Quantifying 13, Barcelona Ingo Scholtes
4 From bug interactions closes reports Change severity assigns assign Quantifying 13, Barcelona Ingo Scholtes to evolving collaboration networks assigns assign Quantifying 13, Barcelona Ingo Scholtes M. Zanetti, I Scholtes, CJ Tessone, F Schweitzer: Categorizing Bugs with Social Networks: A Case Study on Four Open Source Software Communities, Proceedings of ICSE 2013, May
5 to evolving collaboration networks Time t-30d Success at project level? t Quantifying 13, Barcelona Ingo Scholtes M. Zanetti, I Scholtes, CJ Tessone, F Schweitzer: Categorizing Bugs with Social Networks: A Case Study on Four Open Source Software Communities, Proceedings of ICSE 2013, May 2013 to evolving collaboration networks Time t-30d t Quantifying 13, Barcelona Ingo Scholtes M. Zanetti, I Scholtes, CJ Tessone, F Schweitzer: Categorizing Bugs with Social Networks: A Case Study on Four Open Source Software Communities, Proceedings of ICSE 2013, May
6 Predicting successful bug reports 1. in largest connected component? 2. eigenvector centrality 3. closeness centrality 4. betweenness centrality 5. k-coreness 6. total degree 7. in-degree 8. out-degree 9. local clustering coefficient Support Vector Machine valid or faulty? training set Quantifying 13, Barcelona Ingo Scholtes M. Zanetti, I Scholtes, CJ Tessone, F Schweitzer: Categorizing Bugs with Social Networks: A Case Study on Four Open Source Software Communities, Proceedings of ICSE 2013, May 2013 Predicting successful bug reports Firefox Thunderbird Eclipse NetBeans Valid 21 % 23 % 74 % 63 % Precision 82.5 % 90.3 % 88.7 % 78.9 % Recall 44.5 % 38.9 % 91.0 % 87.0 % F-Score Social network analysis allows to predict successful bug reports Precision and recall significantly higher than for competing methods Quantifying 13, Barcelona Ingo Scholtes M. Zanetti, I Scholtes, CJ Tessone, F Schweitzer: Categorizing Bugs with Social Networks: A Case Study on Four Open Source Software Communities, Proceedings of ICSE 2013, May
7 Predicting Future Success Image credit: xkcd.com Quantifying 13, Barcelona Ingo Scholtes Quality matters... but... Can social position of authors predict success of paper? Image credit: ATLAS collaboration, Quantifying 13, Barcelona Ingo Scholtes
8 Coauthorship Network Physics ( ) Quantifying 13, Barcelona Ingo Scholtes Coauthorship Network Physics ( ) Quantifying 13, Barcelona Ingo Scholtes
9 Top 10% paper? measures of social position of authors Coauthorship network [t-2, t] Support Vector Machine top 10% paper in year t+5? training set Published in year t Quantifying 13, Barcelona Ingo Scholtes Social position correlates with future success 5 6 times better than random guess! (CS) Social position indicative for success of only 2580 out of papers Quantifying 13, Barcelona Ingo Scholtes
10 Social position correlates with future success 5 6 times better than random guess! There is room for nobodys to become successful Quantifying 13, Barcelona Ingo Scholtes Good and bad news... Science is done by people (Heisenberg) Social networks influence success Intuition is right Can be quantified Are we in the filter bubble? Do networking (and write good papers) Image credit: wikipedia.org Quantifying 13, Barcelona Ingo Scholtes
11 Thank you! MS Zanetti, I Scholtes, CJ Tessone, F Schweitzer: Categorizing bugs with social networks: a case study on four open source software communities, Proceedings of ICSE 2013, San Francisco, May 2013 MS Zanetti, I Scholtes, CJ Tessone, F Schweitzer: The Rise and Fall of a Central Contributor: Dynamics of Social Organization and Performance in the Gentoo Community, Proceedings of ICSE CHASE 2013, May 2013 MS Zanetti, E Sarigöl, I Scholtes, CJ Tessone, F Schweitzer: A Quantitative Study of Social Organisation in Open Source Software Communities, OpenAccess Series in Informatics, vol. 28, pp , 2012 R Pfitzner, I Scholtes, A Garas, CJ Tessone, F Schweitzer: Betweenness Preference: Quantifying Correlations in the Topological Dynamics of Temporal Networks, Physical Review Letters, Vol. 110, , May I Scholtes, N Wider, R Pfitzner, A Garas, CJ Tessone, F Schweitzer: Slow-down vs. Speed-up of Information Diffusion in Non- Markovian Temporal Networks, arxiv: , July F Schweitzer, E Sarigöl, I Scholtes, A Garas, R Pfitzner: Quantifying Social Inuence on Information Filtering and Measured Scientic Success, working paper, September ischoltes@ethz.ch Quantifying 13, Barcelona Ingo Scholtes
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