Yongqin Gao, Greg Madey Computer Science & Engineering Department University of Notre Dame © Copyright 2002~2003 by Serendip Gao, all rights reserved.

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Presentation transcript:

Yongqin Gao, Greg Madey Computer Science & Engineering Department University of Notre Dame © Copyright 2002~2003 by Serendip Gao, all rights reserved. Modeling and Simulation of the OSS Community Poster for SWARM’2003

© Copyright 2001~2003 by Serendip Gao, all rights reserved. 2 Introduction OSS development  Self-organized, decentralized  Quick, efficient, free Complex network theories  Random network theory  Small world phenomenon  Scale-free network Simulations  Agent-based simulation

© Copyright 2001~2003 by Serendip Gao, all rights reserved. 3 OSS as social network Social network theory  Agents are nodes on graph (developers)  Edges are relationships  Growth of network and preference  Diameter, degree, clusters node or vertex edge or link hub circle of friends or clique developers joint project membership project OSS Social Network project C A BD linchpinweak tie

© Copyright 2001~2003 by Serendip Gao, all rights reserved. 4 Sample network dev[46] dev[83] dev[46] dev[48] dev[46] dev[56] dev[46] dev[58] 6882 dev[58] dev[47] 6882 dev[47] dev[79] 6882 dev[47] dev[52] 6882 dev[47] dev[55] 7028 dev[46] dev[99] 7028 dev[46] dev[51] 7028 dev[46] dev[57] 7597 dev[46] dev[45] 7597 dev[46] dev[72] 7597 dev[46] dev[55] 7597 dev[46] dev[58] 7597 dev[46] dev[61] 7597 dev[46] dev[64] 7597 dev[46] dev[67] 7597 dev[46] dev[70] 9859 dev[46] dev[49] 9859 dev[46] dev[53] 9859 dev[46] dev[54] 9859 dev[46] dev[59] dev[46] dev[83] dev[56] dev[48] dev[52] dev[79] dev[72] dev[51] dev[57] dev[55] dev[99] dev[47] dev[58] dev[53] dev[58] dev[65] dev[45] dev[70] dev[67] dev[59] dev[54] dev[49] dev[64] dev[61] Project 6882 Project 9859 Project 7597 Project 7028 Project OSS Developer - Social Network Developers are nodes / Projects are links 24 Developers 5 Projects 2 Linchpin Developers 1 Cluster

© Copyright 2001~2003 by Serendip Gao, all rights reserved. 5 Degree distribution Distribution of project and Developers in the upper figures. The lower figures are the results of Log-log transformation of the distributions. ‘ + ’ is the data points of the empirical data, and ‘ x ’ is the data points of the simulated data. We also includes the linear polynomial fit in the figures. Solid line is for the empirical data and dashed line is for the simulated data. We can observer that no matter simulated data or empirical data can fit a Straight line. This is the power distribution.

© Copyright 2001~2003 by Serendip Gao, all rights reserved. 6 Cluster distribution We also investigate the cluster distribution of the simulated data. The upper figure is the result in Normal coordinate. And the lower Figure is the distribution in Log-log coordinate. Without considering The tails, we can see the distribution in Log-log coordinate can also fit a straight line. This Is power law in cluster distribution.

© Copyright 2001~2003 by Serendip Gao, all rights reserved. 7 Diameter and clustering coefficient This is the diameter and cluster coefficient developing trends of the simulated data. We can observer that the Diameter is below 8 and decreasing toward 6 with Time. Also the clustering Coefficient is below 0.75 and also decreasing. This Statistics is similar to what we observed in empirical data. The diameter is relatively small according to the size of the network and the clustering coefficient is high. All these properties is the characteristics of scale-free network.

© Copyright 2001~2003 by Serendip Gao, all rights reserved. 8 Developer preference Our simulation is based on bipartite graph. Thus there will be two parts of preference. The project preference is easy to understand – the preference is Proportional to its degree. For developer, the preference is the probability of different actions a developer may choose. By statistical analysis of the empirical data, the developer preference in our model is based on the Developer ’ s degree also. This figure Is the relation between the Probabilities of four different actions and its degree.

© Copyright 2001~2003 by Serendip Gao, all rights reserved. 9 Network evolution Major cluster is the biggest Cluster in a network. The behavior of this cluster may be important for the network, also the developing pattern of this cluster may also reflect The evolution of the network. We inspected the relative size of the major cluster and Observed that the relative size of the major cluster is Slightly increasing and Approach some fixed Percentage of overall network size as shown in the figure.

© Copyright 2001~2003 by Serendip Gao, all rights reserved. 10 Conclusion Power law rules Bipartite nature of collaboration network Call for dynamic fitness Network evolution Life cycle of individual entities Limitation and future work  Insufficient data  More analysis