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The Anatomy of Developer Social          Networks                   Qiaona HONG         Supervisor: Prof. Shing-Chi Cheung...
Social Network                     • Study the Topological Structure of Social                       Network              ...
Research Questions• Q1: What are the similarities and differences  between DSNs and GSNs?                                 ...
Research Questions• Q1: What are the similarities and differences  between DSNs and GSNs?• Q2: How do DSNs evolve over tim...
Research Questions• Q1: What are the similarities and differences  between DSNs and GSNs?• Q2: How do DSNs evolve over tim...
Subjects• Mozilla Bug Report: 2000-2009  – 496,692 bug reports  – 3,893,025 comments• Mozilla CVS Log: 2000-2009  – 44394 ...
DSN Extraction ApproachBug Report 1           Bug Report 2      Bug Report 3          Bug Report 4                        ...
DSN Extraction ApproachBug Report 1             Bug Report 2        Bug Report 3            Bug Report 4                  ...
DSN Extraction ApproachBug Report 1           Bug Report 2          Bug Report 3          Bug Report 4                    ...
DSN Extraction ApproachBug Report 1          Bug Report 2       Bug Report 3          Bug Report 4                        ...
Metrics• Degree Distribution  – The number of edges connected to a node• Degree of Separation  – The shortest path between...
Modularity           A 0.51                             B 0.176• According to A. Clauset’s work, modularity of 0.3 is  a g...
Communities in DSN• Identified Communities in DSN  – Louvain Algorithm (by optimizing modularity)  – 50 different input or...
?       Q1: What are the similarities        and differences between            DSNs and GSNsDegree of Distribution   Degr...
Q1: What are the similarities and differences between DSNs and GSNs        Degree Distribution(1) MozillaDSN-BR           ...
Q1: What are the similarities and differences between DSNs and GSNs        Degree Distribution(1) MozillaDSN-BR           ...
Q1: What are the similarities and differences between DSNs and GSNs          Degree Distribution• Quantitative power law f...
Q1: What are the similarities and differences between DSNs and GSNs P-value<0.1       Degree                 some<0.1,othe...
Q1: What are the similarities and differences between DSNs and GSNs                                                       ...
Q1: What are the similarities and differences between DSNs and GSNs                                                       ...
Q1: What are the similarities and differences between DSNs and GSNs                                                       ...
Q1: What are the similarities and differences between DSNs and GSNs                                                      M...
Q1: What are the similarities and differences between DSNs and GSNs           Community Size(1) MozillaDSN-BR             ...
Q1: What are the similarities and differences between DSNs and GSNs           Community Size                              ...
Q1: What are the similarities and differences between DSNs and GSNs           Community Size     21%-36%                  ...
?     Q4:What are the similarities and     differences between DSNs extracted     using different social linkage indicator...
Q2: How do DSNs evolve over time?     Change of Developer SizeDSNs-BR always have more developers than DSNs-CL            ...
Q2: How do DSNs evolve over time?Change of Percentage of New Comers  DSNs-BR always have higher percentage of new         ...
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
The Anatomy of Developer Social Networks
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The Anatomy of Developer Social Networks

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  • Metrics to analyze the social networkTechniques to visualize the social networkFinding influential peopleFinding communityInformation diffusionRecommendationStudy the Topological Structure of Social Network[1] Y. Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong, &quot;Analysis of topological characteristics of huge online social networking services,&quot; in WWW &apos;07: Proceedings of the 16th international conference on World Wide Web. New York, NY, USA: ACM, 2007, pp. 835-844.[2] A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, &quot;Measurement and analysis of online social networks,&quot; in Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, ser. IMC &apos;07. New York, NY, USA: ACM, 2007, pp. 29-42.Study the Community Structure of Social Network[1] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, &quot;Fast unfolding of communities in large networks,&quot; Journal of Statistical Mechanics: Theory and Experiment, vol. 2008, no. 10, pp. P10 008+, Jul. 2008.[2] Y. R. Lin, H. Sundaram, Y. Chi, J. Tatemura, and B. L. Tseng, &quot;Blog community discovery and evolution based on mutual awareness expansion,&quot; in WI &apos;07: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence. Washington, DC, USA: IEEE Computer Society, 2007, pp. 48-56.Study the Topological Structure of Social NetworkDegree distribution [Y. Y. Ahn @WWW &apos;07]Power-law, small-world [A. Mislove@IMC &apos;07]Study the Community Structure of Social NetworkCommunity structure extraction method[V. D. Blondel@ Journal of Statistical Mechanics: Theory and Experiment]Evolution of community, community evolution patterns [Y. R. Lin@WI &apos;07]Techniques to visualize the social networkCommunity structure extraction method[V. D. Blondel@ Journal of Statistical Mechanics: Theory and Experiment]Finding Influential PeopleCommunity structure extraction method[V. D. Blondel@ Journal of Statistical Mechanics: Theory and Experiment]Information DiffusionCommunity structure extraction method[V. D. Blondel@ Journal of Statistical Mechanics: Theory and Experiment]
  • A nature question to ask here is that ..
  • Apart from Q1, in this thesis, we also study other research question, cite my paper here [very important]
  • Apart from Q1, in this thesis, we also study other research question, cite my paper here [very important]
  • The subjects used for this study are Mozilla Bug Report, Mozilla CVS Log, Eclipse Bug Report, Eclipse CVS Log.Both Mozilla and Eclipse are very successful open source projects.To compare with GSN, we extract DSNs from these two projects.
  • Why I used these metrics? I need to polish this slide by using more formal sentences.
  • [8] A. Clauset, M. E. J. Newman, and C. Moore, &quot;Finding community structure in very large networks,&quot; Aug. 2004.
  • BOF meetings. Developer are free to join the BOF meetings. So we consider BOF meetings reflect real communities.One identified community may contain more than one BOF meetings. However one BOF only be contained in one identified community.Which means BOF represent finer division of developers and Our identified communities reflect real communities.
  • Why this question? There are many possibilities. Please list some here.
  • To compare with GSN, we extract DSN from different length of time 1-month, 3-month, 6-month, 1-year, 2-year ,4-years. Possible result, my effort is not trivial.How to interpret the graph.
  • To compare with GSN, we extract DSN from different length of time 1-month, 3-month, 6-month, 1-year, 2-year ,4-years. Possible result, my effort is not trivial.
  • To compare with GSN, we extract DSN from different length of time 1-month, 3-month, 6-month, 1-year, 2-year ,4-years. Possible result, my effort is not trivial.
  • To compare with GSN, we extract DSN from different length of time 1-month, 3-month, 6-month, 1-year, 2-year ,4-years.
  • I need more text on the slides
  • 28%
  • 28%
  • This is a GREAT slide. Be sure to explain Extinct and Emerge well since both has “empty” on one side of the arrow.
  • In the paper, we examine the community evolution from 2000 to 2009, here we use the period from 2005 to 2009 to illustrate our findings.This is also a very good slide. I like the tracking of different paths of communities over time.
  • In the paper, we examine the community evolution from 2000 to 2009, here we use the period from 2005 to 2009 to illustrate our findings.This is also a very good slide. I like the tracking of different paths of communities over time.
  • In the paper, we examine the community evolution from 2000 to 2009, here we use the period from 2005 to 2009 to illustrate our findings
  • [1] Xin Yang, RaulaGaikovina Kula, Camargo Cruz Ana Erika, Norihiro Yoshida, KazukiHamasaki, Kenji Fujiwara, and Hajimu Iida, &quot;Understanding OSS Peer Review Roles in Peer Review Social Network (PeRSoN),&quot; In Proceedings of the 19th Asia-Pacific Software Engineering Conference (APSEC2012), (to appear)
  • Xin Yang in their work, they used our approach for peer review system to generate a peer review social networks. Based on this review social networks, they target to investigate the importance of OSS peer review contributor roles and their review activities.JifengXuan, He Jiang, ZhileiRen, WeiqinZou, “Developer Prioritization in Bug Repositories”, In Proceedings of the 34th International Conference on Software Engineering (ICSE 2012), pp. 25-35, 2012. Y. Tian, P. Achananuparp, I. Lubis, D. Lo, and E.-P. Lim. What does software engineering community microblog about? In MSR, 2012.To investigate the importance of OSS peer-review contributers and review activities.
  • Xin Yang in their work, they used our approach for peer review system to generate a peer review social networks. Based on this review social networks, they target to investigate the importance of OSS peer review contributor roles and their review activities.JifengXuan, He Jiang, ZhileiRen, WeiqinZou, “Developer Prioritization in Bug Repositories”, In Proceedings of the 34th International Conference on Software Engineering (ICSE 2012), pp. 25-35, 2012. Y. Tian, P. Achananuparp, I. Lubis, D. Lo, and E.-P. Lim. What does software engineering community microblog about? In MSR, 2012.To investigate the importance of OSS peer-review contributers and review activities.
  • files are likely to be vulnerable when changed by many developers who have made many changes to other files. Practitioners can use these observations to prioritize securi
  • Transcript of "The Anatomy of Developer Social Networks"

    1. 1. The Anatomy of Developer Social Networks Qiaona HONG Supervisor: Prof. Shing-Chi Cheung 1
    2. 2. Social Network • Study the Topological Structure of Social Network – Y. Y. Ahn @WWW 07; A. Mislove@IMC 07 • Study the Community Structure of Social Network – V. D. Blondel@ Journal of Statistical Mechanics: Theory and Experiment; Y. R. Lin@WI 07 • Techniques to visualize the social network – Jeffrey Heer@InfoVis 05 • Influential People & Information DiffusionGeneral Social Network – Kimura, M.@InfoVis 07 (GSN) • Friend Recommendation – Nitai B. Silva@WCCI‘10 2
    3. 3. Research Questions• Q1: What are the similarities and differences between DSNs and GSNs? 3
    4. 4. Research Questions• Q1: What are the similarities and differences between DSNs and GSNs?• Q2: How do DSNs evolve over time?• Q3: How do communities evolve in DSNs?• Q4: What are the similarities and differences between DSNs extracted using different social linkage indicators? 4
    5. 5. Research Questions• Q1: What are the similarities and differences between DSNs and GSNs?• Q2: How do DSNs evolve over time?• Q3: How do communities evolve in DSNs?•Qiaona HONG, the similarities and differences Q4: What are Sunghun Kim, S.C. Cheung and Christian Bird, “Understanding a different social between DSNs extracted using Developer Social Network indicators? linkage and its Evolution”, in Proceedings of the27th IEEE International Conference on SoftwareMaintenance, 2011. 5
    6. 6. Subjects• Mozilla Bug Report: 2000-2009 – 496,692 bug reports – 3,893,025 comments• Mozilla CVS Log: 2000-2009 – 44394 revisions• Eclipse Bug Report: 2002-2009 – 294,938 bug reports – 1,618,667 comments• Eclipse CVS Log: 2002-2009 – 22493 revisions 6
    7. 7. DSN Extraction ApproachBug Report 1 Bug Report 2 Bug Report 3 Bug Report 4 David Comment 1David Comment 1 Bob Comment 1 Bob Comment 2 Bob Comment 2Bob Comment 2 Jack Comment 2 Jack Comment 3 Jack Comment 3Jack Comment 3 Bill Comment 3 Bill Comment 3 David Bill Bob Jack 7
    8. 8. DSN Extraction ApproachBug Report 1 Bug Report 2 Bug Report 3 Bug Report 4 David Comment 1David Comment 1 Bob Comment 1 Bob Comment 2 Bob Comment 2Bob Comment 2 Jack Comment 2 Jack Comment 3 Jack Comment 3Jack Comment 3 Bill Comment 3 Bill Comment 3 1 David Bill 2 2 2 2 4 Bob Jack 8
    9. 9. DSN Extraction ApproachBug Report 1 Bug Report 2 Bug Report 3 Bug Report 4 David Comment 1David Comment 1 Bob Comment 1 Bob Comment 2 Bob Comment 2Bob Comment 2 Jack Comment 2 Jack Comment 3 Jack Comment 3Jack Comment 3 Bill Comment 3 Bill Comment 3 David Bill 4 Bob Jack 9
    10. 10. DSN Extraction ApproachBug Report 1 Bug Report 2 Bug Report 3 Bug Report 4 David Comment 1David Comment 1 Bob Comment 1 Bob Comment 2 Bob Comment 2Bob Comment 2 Jack Comment 2 Jack Comment 3 Jack Comment 3Jack Comment 3 Bill Comment 3 Bill Comment 3 Bob Jack 10
    11. 11. Metrics• Degree Distribution – The number of edges connected to a node• Degree of Separation – The shortest path between two nodes• Modularity – To measure the quality of division of nodes• Community Size – The number of nodes within a community 11
    12. 12. Modularity A 0.51 B 0.176• According to A. Clauset’s work, modularity of 0.3 is a good indicator of significant community structure in a network• When the modularity is 0, the community structure is no stronger than that of a randomly generated network 12
    13. 13. Communities in DSN• Identified Communities in DSN – Louvain Algorithm (by optimizing modularity) – 50 different input ordering of nodes 13
    14. 14. ? Q1: What are the similarities and differences between DSNs and GSNsDegree of Distribution Degree of SeparationModularity Community Size 14
    15. 15. Q1: What are the similarities and differences between DSNs and GSNs Degree Distribution(1) MozillaDSN-BR (2) MozillaDSN-CL(3) EclipseDSN-BR (4) EclipseDSN-CL 15
    16. 16. Q1: What are the similarities and differences between DSNs and GSNs Degree Distribution(1) MozillaDSN-BR (2) MozillaDSN-CL(3) EclipseDSN-BR (4) EclipseDSN-CL 16
    17. 17. Q1: What are the similarities and differences between DSNs and GSNs Degree Distribution• Quantitative power law fit test – An approach of analyzing power law distributed data introduced by A. Clauset et al.• P-value : The likelihood that(2) MozillaDSN-CL (1) MozillaDSN-BR degree distribution does actually follow a power-law – If p-value is less than 0.1, the power law is rejected. (3) EclipseDSN-BR (4) EclipseDSN-CL 17
    18. 18. Q1: What are the similarities and differences between DSNs and GSNs P-value<0.1 Degree some<0.1,other>0.1 Distribution (1) MozillaDSN-BR (2) MozillaDSN-CLDifferent from GSNs, DSNs do not(4) EclipseDSN-CL (3) EclipseDSN-BR follow power-law 18
    19. 19. Q1: What are the similarities and differences between DSNs and GSNs Degree of Separation Degree of Separation Degree ofof Separation Degree Separation Degree of Separation 1-month DSN 1-month DSN 1-year DSN Degree ofDSN 1-year DSN tw itter(8000 sample) Separation twtw itter(8000 sample) tw itter(8000 sample) Degree of Separation 1-month DSN 1-month DSN 1-month DSN 3-month DSN 3-month DSN 1-month DSN 3-month DSN 6-month DSN 3-month 3-month DSN 6-month DSN 3-month DSN 6-month DSN 6-month DSN 6-month DSN 1-year DSN 1-year DSN 1-year 2-year DSN 2-year DSN 1-year DSN 2-year DSN 4-year DSN 2-year 2-year DSN 4-year DSN 2-year DSN 4-year DSN 4-year DSN 4-year DSN itter(8000 sample) tw itter(8000 sample) cyw orld(3000 sample) cyw orld(3000 sample) cyw orld(3000 sample) tw cyw orld(3000 sample) cyw orld(3000 sample) itter(8000 sample) cyw orld(3000 sample) 1.0 6-month DSN 4-year DSN 1.0 0.0 0.00.2 0.20.4 0.40.6 0.60.8 0.81.0 1.0 1.0 0.8 1.0 1-month DSN 0.6 0.6 0.0 0.0 0.2 0.2 0.4 0.4 0.6 0.6 0.6 0.6 0.8 3-month DSN Degree of Separa 0.8 0.6 0.8 6-month DSN 0.6 0.4 MozillaDSN-CL 0.4 MozillaDSN-BR MozillaDSN-CL 0.6 0.4 0.6 MozillaDSN-BR 0.4 0.4 MozillaDSN-BR MozillaDSN-BR MozillaDSN-BR 1-month DSN MozillaDSN-CL MozillaDSN-CL MozillaDSN-CL 1-year DSN MozillaDSN-CL 0.4 Degree of Separation 2-year DSN 3-month DSN MozillaDSN-BR 0.4 0.2 0.4 0.2 0.2 Probability 6-month DSN 4-year DSN Probability 0.2 0.2 Probability Probability 0.6 0.2 0.2 0.0 0.2 1-month DSN 1-year DSN tw itter(8000 sample)Probability 1.0 3-month DSN 2-year DSN cyw orld(3000 sample) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6-month DSN 4-year DSN 0 0 2 2 4 4 6 6 8 8 10 12 14 16 18 10 12 14 16 18 0 0 2 2 4 4 6 6 8 10 12 14 16 18 8 10 12 14 16 18 0.6 0 0 2 2 4 4 6 6 8 8 1010 1212 1414 1616 1818 0 2 4 6 8 10 12 14 16 18 0 0 2 2 4 4 6 6 8 8 1010 1212 1414 1616 1818 0 2 4 6 8 10 12 14 16 18 0.4 0.8 Mozilla 1.0 0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18 0.6 0.6 0.6 0.6 0.0 0.0 0.2 0.2 0.4 0.4 0.6 0.6 0.6 0.6 0.6 0.6 Probability0.2 0.4 0.4 0.6 0.6 0.6 0.6 0.8 0.4 MozillaDSN-BR 0.2 0.4 0.4 0.4 0.4 Probability EclipseDSN-BR EclipseDSN-BR EclipseDSN-CL EclipseDSN-CL 0.4 0.4 0.4 0.4 EclipseDSN-BR EclipseDSN-BR EclipseDSN-BR EclipseDSN-CL EclipseDSN-CL EclipseDSN-CL 0.4 0.6 EclipseDSN-BR EclipseDSN-CL 0.4 MozillaDSN-BR MozillaDSN-CL 0.2 0.2 0.2 0.2 0.0 0.0 0.2 0.2 0.2 0.2 0.2 0.2 0.0 0.2 0.4 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 obability 0 2 4 6 8 10 1 0 0 2 2 4 4 6 6 8 8 10 12 14 16 18 10 12 14 16 18 0 0 2 2 4 4 6 6 8 8 10 12 14 16 18 10 12 14 16 18 0.2 0.0 0.0 0 0 2 2 4 4 6 6 8 8 1010 1212 1414 1616 1818 0 2 4 6 8 10 12 14 16 18 0 0 2 2 4 4 6 6 8 8 1010 1212 1414 1616 1818 0 2 4 6 8 10 12 14 16 18 Distance between two developers Distance between two developers 0 2 4 6 8 Distance between two 2 developers 12 14 16 18 0 developers Distance between two 4 6 8 10 Distance between two developers 10 12 14 16 18 0 2 4 6 Distance between two developers 8 10 12 14 16 18 19 0 2 0.6 0.0 0.0
    20. 20. Q1: What are the similarities and differences between DSNs and GSNs Degree of Separation Degree ofof SeparationDegree of Separation Degree Separation Degree of Separation 1-month DSN 1-year DSN Degree ofDSN tw itter(8000 sample) Separation twtw itter(8000 sample) 1-month DSN 1-month DSN 1-month DSN 3-month DSN 3-month DSN DSN 6-month DSN 3-month 3-month DSN 1-month 6-month DSN 6-month DSN 6-month DSN 3-month DSN Degree of Separation 1-year DSN 2-year DSN 1-year 1-year DSN 1-month DSN 2-year DSN DSN 4-year DSN 2-year 2-year DSN 1-year 4-year DSN 4-year DSN 4-year DSN 3-month DSN 2-year DSN itter(8000 sample) tw itter(8000 sample) cyw orld(3000 sample) cyw 1-year sample) cyworld(3000 DSN cyw orld(3000 sample) orld(3000 sample) tw itter(8000 sample) 2-year DSN cyw orld(3000 sample) tw itter(8000 s cyw orld(3000 1.0 6-month DSN 4-year DSN 6-month DSN 4-year DSN 0.0 0.00.2 0.20.4 0.40.6 0.60.8 0.81.0 1.0 1.0 0.8 1.0 1-month DSN 4.12 0.6 0.0 0.0 0.2 0.2 0.4 0.4 0.6 0.6 0.6 0.6 3-month DSN Degree of Separa 1.0 0.8 0.6 0.8 6-month DSN 0.4 MozillaDSN-BR MozillaDSN-CL 0.6 0.4 0.6 0.6 0.4 90% (6) 0.4 MozillaDSN-BR MozillaDSN-BR MozillaDSN-BR 1-month DSN MozillaDSN-CL MozillaDSN-CL MozillaDSN-CL 1-year DSN 0.8 MozillaDSN-CL Degree of Separation 2-year DSN 3-month DSN MozillaDSN-BR 0.4 0.2 0.4 0.2 Probability 6-month DSN 4-year DSN 0.2 0.2 Probability Probability 0.6 0.6 0.2 0.0 0.2 1-month DSN 1-year DSN tw itter(8000 sample) 0.4Probability MozillaDSN-BR 2-year DSN Mozill 1.0 3-month DSN cyw orld(3000 sample) 0.0 0.0 0.0 0.0 0.0 6-month DSN 2 4 6 8 10 DSN14 4-year 12 0.4 0 2 4 6 8 10 12 14 16 18 0 16 18 0.6 0 0 2 2 4 4 6 6 8 8 1010 1212 1414 1616 1818 0 2 4 6 8 10 12 14 16 18 0 0 2 2 4 4 6 6 8 8 1010 1212 1414 1616 1818 0 2 4 6 8 10 12 14 16 18 0.4 0.8 Mozilla 0.2 1.0 0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18 Probability 0.6 0.6 0.2 0.0 0.0 0.2 0.2 0.4 0.4 0.6 0.6 0.6 0.6 0.6 0.6 Probability0.2 0.4 0.4 0.6 0.6 0.6 0.6 0.8 0.4 MozillaDSN-BR 0.2 0.4 0.4 Probability EclipseDSN-BR EclipseDSN-CL 0.4 0.4 0.4 0.0 0.0 0.4 EclipseDSN-BR EclipseDSN-BR EclipseDSN-BR EclipseDSN-CL EclipseDSN-CL EclipseDSN-CL 0.4 0.6 EclipseDSN-BR EclipseDSN-CL 0.4 0 2 4 6 8 10 MozillaDSN-BR 16 12 14 18 0 2 4 6 MozillaDSN-CL 8 10 0.2 0.2 0.0 0.0 0.2 0.2 0.2 0.2 0.2 0.2 0.0 0.2 0.4 0.0 0.0 0.6 0.6 0.2 0.0 0.0 0.0 0.0 obability 0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 1 0.2 0.0 0.0 0 0 2 2 4 4 6 6 8 8 1010 1212 1414 1616 1818 0 2 4 6 8 10 12 14 16 18 0 0 2 2 4 4 6 6 8 8 1010 1212 1414 1616 1818 0 2 4 6 8 10 12 14 16 18 Distance between two developers 0 2 4 6 8 Distance between two 2 developers 12 14 16 18 0 developers Distance between two 4 6 8 10 Distance between two developers 10 12 14 16 18 0 2 4 Distance between two developers 6 8 10 12 14 16 18 20 0 2 0.6 0.0 0.0 .4 .4 EclipseDSN-BR Eclips
    21. 21. Q1: What are the similarities and differences between DSNs and GSNs Degree of Separation Degree of Separation Degree ofof Separation Degree Separation Degree of Separation 1-month DSN 1-month DSN 1-year DSN Degree ofDSN 1-year DSN tw itter(8000 sample) Separation twtw itter(8000 sample) tw itter(8000 sample) Degree of Separation 1-month DSN 1-month DSN 1-month DSN 3-month DSN 3-month DSN 1-month DSN 3-month DSN 6-month DSN 3-month 3-month DSN 6-month DSN 3-month DSN 6-month DSN 6-month DSN 6-month DSN 1-year DSN 1-year DSN 1-year 2-year DSN 2-year DSN 1-year DSN 2-year DSN 4-year DSN 2-year 2-year DSN 4-year DSN 2-year DSN 4-year DSN 4-year DSN 4-year DSN itter(8000 sample) tw itter(8000 sample) cyw orld(3000 sample) cyw orld(3000 sample) cyw orld(3000 sample) tw cyw orld(3000 sample) cyw orld(3000 sample) itter(8000 sample) cyw orld(3000 sample) 1.0 6-month DSN 4-year DSN 1.0 0.0 0.00.2 0.20.4 0.40.6 0.60.8 0.81.0 1.0 1.0 0.8 1.0 1-month DSN 0.6 0.6 3.0 2.1 0.0 0.0 0.2 0.2 0.4 0.4 0.6 0.6 0.6 0.6 0.8 3-month DSN Degree of Separa 0.8 0.6 0.8 6-month DSN 0.6 0.4 MozillaDSN-CL 0.4 MozillaDSN-BR MozillaDSN-CL 0.6 0.4 0.6 MozillaDSN-BR 0.4 0.4 MozillaDSN-BR MozillaDSN-BR MozillaDSN-BR 1-month DSN MozillaDSN-CL MozillaDSN-CL MozillaDSN-CL 1-year DSN MozillaDSN-CL 0.4 Degree of Separation 2-year DSN 3-month DSN MozillaDSN-BR 0.4 0.2 0.4 0.2 0.2 Probability 6-month DSN 4-year DSN Probability 0.2 0.2 Probability Probability 0.6 0.2 0.2 0.0 0.2 1-month DSN 1-year DSN tw itter(8000 sample)Probability 1.0 3-month DSN 2-year DSN cyw orld(3000 sample) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6-month DSN 4-year DSN 0 0 2 2 4 4 6 6 8 8 10 12 14 16 18 10 12 14 16 18 0 0 2 2 4 4 6 6 8 10 12 14 16 18 8 10 12 14 16 18 0.6 0 0 2 2 4 4 6 6 8 8 1010 1212 1414 1616 1818 0 2 4 6 8 10 12 14 16 18 0 0 2 2 4 4 6 6 8 8 1010 1212 1414 1616 1818 0 2 4 6 8 10 12 14 16 18 0.4 0.8 Mozilla 1.0 0 2 4 6 8 10 12 14 16 18 0 2 4 6 8 10 12 14 16 18 0.6 0.6 0.6 0.6 0.0 0.0 0.2 0.2 0.4 0.4 0.6 0.6 0.6 0.6 0.6 0.6 Probability0.2 0.4 0.4 0.6 0.6 4.0 2.5 0.6 0.6 0.8 0.4 MozillaDSN-BR 0.2 0.4 0.4 0.4 0.4 Probability EclipseDSN-BR EclipseDSN-BR EclipseDSN-CL EclipseDSN-CL 0.4 0.4 0.4 0.4 EclipseDSN-BR EclipseDSN-BR EclipseDSN-BR EclipseDSN-CL EclipseDSN-CL EclipseDSN-CL 0.4 0.6 EclipseDSN-BR EclipseDSN-CL 0.4 MozillaDSN-BR MozillaDSN-CL 0.2 0.2 0.2 0.2 0.0 0.0 0.2 0.2 0.2 0.2 0.2 0.2 0.0 0.2 0.4 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 obability 0 0 2 2 4 4 6 6 8 8 10 12 14 16 18 to 12 14 16 1818 2 4 6 0 0 2 2 4 4 6 6 8 8 10 12each 18 10 12 14 16 18 10 12 14 16 0 Developers in DSN are much 44closer1010 1212 1414 1616other than18 8 10 1 0.2 0.0 0.0 0 0 2 2 4 4 6 6 8 8 1010 1212 1414 1616 1818 0 2 4 6 8 10 12 14 16 1800 22 4 0 2 66 88 6 8 10 14 16 18 Distance between two developers Distance participants in GSN. Distance 16 18 between two developers 0 2 4 6 Distance between two 2 developers 12 14 0 developers between two 4 6 8 8 10 Distance between two developers 10 12 14 16 18 0 2 4 Distance between two developers 6 8 10 12 14 16 18 21 0 2 0.6 0.0 0.0
    22. 22. Q1: What are the similarities and differences between DSNs and GSNs Modularity Modularity MozillaDSN-CL 0.7 0.6 0.5 0.4 0.3 MozillaDSN-BR 0.7 0.6 0.5 0.4 Modularity 0.3 EclipseDSN-CL 0.7 0.6 0.5 0.4 0.3 EclipseDSN-BR 0.7 0.6 0.5 0.4 0.3 ok SN SN SN rld N N N DS DS DS bo wo D D D ce th th th ar ar ar Cy on on on Fa ye ye ye m m m 1- 2- 4- 1- 3- 6- NetworkSimilar to GSNs, all DSNs have significant community structure 22
    23. 23. Q1: What are the similarities and differences between DSNs and GSNs Community Size(1) MozillaDSN-BR (2) MozillaDSN-CL(3) EclipseDSN-BR (4) EclipseDSN-CL 23
    24. 24. Q1: What are the similarities and differences between DSNs and GSNs Community Size 28%(1) MozillaDSN-BR (2) MozillaDSN-CL(3) EclipseDSN-BR (4) EclipseDSN-CL 24
    25. 25. Q1: What are the similarities and differences between DSNs and GSNs Community Size 21%-36% 23%-43%(1) MozillaDSN-BR (2) MozillaDSN-CL 15%-30% 23%-33%(3) EclipseDSN-BR (4) EclipseDSN-CL 25
    26. 26. ? Q4:What are the similarities and differences between DSNs extracted using different social linkage indicators Q2: How do DSNs evolve over time?Degree of Distribution Degree of SeparationModularity Community Size 26
    27. 27. Q2: How do DSNs evolve over time? Change of Developer SizeDSNs-BR always have more developers than DSNs-CL 27
    28. 28. Q2: How do DSNs evolve over time?Change of Percentage of New Comers DSNs-BR always have higher percentage of new comers than DSNs-CL 28
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