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Computational Discovery in Evolving Complex Networks. Yongqin Gao Advisor: Greg Madey. Outline. Background Methodology for Computational Discovery Problem Domain – OSS Research Process I: Data Mining Process II: Network Analysis Process III: Computer Simulation

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  • Background

  • Methodology for Computational Discovery

  • Problem Domain – OSS Research

  • Process I: Data Mining

  • Process II: Network Analysis

  • Process III: Computer Simulation

  • Process IV: Research Collaboratory

  • Contributions

  • Conclusion and Future Work


  • Network research gains more attentions

    • Internet

    • Communication network

    • Social network

    • Software developer network

    • Biological network

  • Understanding the evolving complex network

    • Goal I: Search

    • Goal II: Prediction

  • Computational scientific discovery

Computational discovery our methodology
Computational DiscoveryOur Methodology

Problem domain
Problem Domain

  • Open Source Software Movement

    • What is OSS

      • Free to use, modify and distribute and source code available and modifiable

      • Potential advantages over commercial software: Potentially high quality; Fast development; Low cost

    • Why study OSS (Goal)

      • Software engineering — new development and coordination methods

      • Open content — model for other forms of open, shared collaboration

      • Complexity — successful example of self-organization/emergence

Glory of oss number of active apache hosts
Glory of OSSNumber of Active Apache Hosts

Problem domain1
Problem Domain

  • community

    • The biggest OSS development communities

    • 134,751 registered projects

    • 1,439,773 registered users

Problem domain2
Problem Domain

  • Our Data Set

    • 25 monthly dumps since January 2003.

    • Totally 460G and growing at 25G/month.

    • Every dump has about 100 tables.

    • Largest table has up to 30 million records.

  • Experiment Environment

    • Dual Xeon 3.06GHz, 4G memory, 2T storage

    • Linux 2.4.21-40.ELsmp with PostgreSQL 8.1

Related research
Related Research

  • OSS research

    • W. Scacchi, “Free/open source software development practices in the computer game community”, IEEE Software, 2004.

    • C. Kevin, A. Hala and H. James, “Defining open source software project success”, 24th International Conference on Information Systems, Seattle, 2003.

  • Complex networks

    • L.A. Adamic and B.A. Huberman, “Scaling behavior of the world wide web”, Science, 2000.

    • M.E.J. Newman, “Clustering and preferential attachment in growing networks”, Physics Review, 2001.

Process i data mining
Process I: Data Mining

  • Related Research:

    • S. Chawla, B. Arunasalam and J. Davis, “Mining open source software (OSS) data using association rules network”, PAKDD, 2003.

    • D. Kempe, J. Kleinberg and E. Tardos, “Maximizing the spread of influence through a social network”, SIGKDD, 2003.

    • C. Jensen and W. Scacchi, “Data mining for software process discovery in open source software development communities”, Workshop on Mining Software Repositories, 2004.

Process i data mining1


Algorithm Application

Feature Selection

Relevant data

Data Purging


Data Preparation

Process I: Data Mining

Raw data

Process i data mining2
Process I: Data Mining

  • Data Preparation

    • Data discovery

      • Locating the information

    • Data characterization

      • Activity features: user categorization

      • Network features

    • Data assembly

  • Data Purging

    • Treatment about data inconsistency

      • Unifying the date presentation by loading into single depository

    • Treatment about data pollution

      • Removing “inactive” projects

  • Feature Selection

    • This method is used to remove dependent or insignificant features.

    • NMF (Non-negative Matrix Factorization)

Process i data mining3
Process I: Data Mining

  • Result I

    • Significant features

      • By feature selection, we can identify the significant feature set describing the projects.

      • Activity features: “file_releases”, “followup_msg”, “support_assigned”, “feature_assigned” and task related features

      • Network features: “degrees”, “betweenness” and “closeness”

Process i data mining4
Process I: Data Mining

  • Distribution-based clustering (Christley, 2005)

    • Clustering according to the distribution of features instead of values of individual feature

    • We assume every entity (project) has an underlying distribution of the feature set (activity features)

    • Using statistical hypothesis test

      • Non-parametric test

      • Fisher’s contingency-table test is used

        • Joachim Krauth, “Distribution-free statistics: an application-oriented approach”, Elsevier Science Publisher, 1988.

Process i data mining5
Process I: Data Mining

  • Procedure:

    While (still unclustered entities)

    Put all unclustered entities into one cluster

    While (some entities not yet pairwise compared)

    A = Pick entity from cluster

    For each other entity, B, in cluster not yet compared to A

    Run statistical test on A and B

    If significant result

    Remove B from cluster

  • Worst case complexity: O(n2)

Process i data mining6
Process I: Data Mining

  • Result II

  • Unsupervised learning

    • Distribution-based method used to cluster the project history using the activity distribution

    • We named the clusters using ID and the results are shown in the table

    • High support and confidence in evaluation

Process i data mining7
Process I: Data Mining

  • Two sample distributions from different categories

  • Unbalanced feature distribution → could be “unpopular”

  • Balanced feature distribution → could be “popular”

Process i data mining8
Process I: Data Mining

  • Discoveries in Process I

    • Significant feature set selection

      • Network features are important

      • Further inspection in next process

    • Distribution based predictor

      • Based on the activity feature distribution

      • Prediction of the “popularity” based on the balance of the activity feature distribution

  • Benefit of these discoveries

    • For collaboration based communities, these discoveries can help in resource allocation optimization.

Process ii network analysis
Process II: Network Analysis

  • Why network analysis

    • Assess the importance of the network measures to the whole network and to individual entity in the network

    • Inspect the developing patterns of these network measures

  • Network analysis

    • Structure analysis

    • Centrality analysis

    • Path analysis

Process ii network analysis1
Process II: Network Analysis

  • Related research:

    • P. Erdös and A. Rényi, “On random graphs”, Publicationes Mathematicae, 1959.

    • D.J. Watts and S. H. Strogatz, “Collective dynamics of small-world networks”, Nature, 1998.

    • R. Albert and A.L. Barabάsi, “Emergence of scaling in random networks”, Science, 1999.

    • Y. Gao, “Topology and evolution of the open source software community”, Master Thesis, 2003.

Process ii network analysis2
Process II: Network Analysis

  • Structure Analysis

    • Understanding the influence of the network structure to individual entities in the network

    • Inspected measures

      • Approximate diameter

      • Approximate clustering coefficient

      • Component distribution

Process ii network analysis3
Process II: Network Analysis

  • Conversion among C-NET, P-NET and D-NET

Process ii network analysis4
Process II: Network Analysis

  • Result I

    • Approximate Diameters

      • D-NET: between (5,7) while network size ranged from 151,803 to 195,744.

      • P-NET: between (6,8) while network size ranged from 123,192 to 161,798.

    • Approximate Clustering Coefficient

      • D-NET: between (0.85, 0.95)

      • P-NET: between (0.65, 0.75)

Process ii network analysis6
Process II: Network Analysis

  • Centrality Analysis

    • Understanding the importance of individual entities to the global network structure

    • Inspected measures:

      • Average Degrees

      • Degree Distributions

      • Betweenness

      • Closeness

Process ii network analysis7
Process II: Network Analysis

  • Result II

    • Average Degrees

      • Developer degree in C-NET: 1.4525

      • Project degree in C-NET: 1.7572

      • Developer degree in D-NET: 12.3100

      • Project degree in P-NET: 3.8059

Process ii network analysis8
Process II: Network Analysis

  • Result II (Degree distributions in C-NET)

Process ii network analysis9
Process II: Network Analysis

  • Result II (Degree distributions in D-NET and P-NET)

Process ii network analysis10
Process II: Network Analysis

  • Result II

    • Average Betweenness

      • P-NET: 0.2669e-003

    • Average Closeness

      • P-NET: 0.4143e-005

    • Normally these two measures yield very small value in large networks (N>10,000).

Process ii network analysis11
Process II: Network Analysis

  • Path Analysis

    • Understanding the developing patterns of the network structure and individual entities in the network

    • Inspected measures:

      • Active Developer Percentage

      • Average Degrees

      • Diameters

      • Clustering coefficients

      • Betweenness

      • Closeness

Process ii network analysis12
Process II: Network Analysis

  • Result III (Active entities)

Process ii network analysis13
Process II: Network Analysis

  • Result III (Average degrees in C-NET)

Process ii network analysis14
Process II: Network Analysis

  • Result III (Average degrees in D-NET and P-NET)

Process ii network analysis15
Process II: Network Analysis

  • Result III (Diameters in D-NET and P-NET)

Process ii network analysis16
Process II: Network Analysis

  • Result III (Clustering coefficients for D-NET and P-NET)

Process ii network analysis17
Process II: Network Analysis

  • Result III (Average betweenness and closeness for P-NET)

Process ii network analysis19
Process II: Network Analysis

  • Discoveries in Process II:

    • Measures of structure analysis and centrality analysis all indicate very high connectivity of the network.

    • Measures of path analysis reveal the developing patterns of these measures (life cycle behavior).

  • Benefits of these discoveries

    • High connectivity in a network is an important feature for information propagation, failure proof. Understanding this discovery can help us improve our practices in collaboration networks and communication networks.

    • Understanding the developing patterns of these network measures provides us a method to monitor network development and to improve the network if necessary.

Process iii computer simulation
Process III: Computer Simulation

  • Related Research:

    • P.J. Kiviat, “Simulation, technology, and the decision process”, ACM Transactions on Modeling and Computer Simulation,1991.

    • R. Albert and A.L. Barabási, “Emergence of scaling in random networks”, Science, 1999.

    • J. Epstein R. Axtell, R. Axelrod and M. Cohen, “Aligning simulation models: A case study and results”, Computational and Mathematical Organization Theory, 1996.

    • Y. Gao, “Topology and evolution of the open source software community”, Master Thesis, 2003.

Process iii computer simulation1
Process III: Computer Simulation

  • Iterative simulation method

    • Empirical dataset

    • Model

    • Simulation

  • Verification and validation

    • More measures

    • More methods

Process iii computer simulation2
Process III: Computer Simulation

  • Previous iterated models (master thesis):

    • Adapted ER Model

    • BA Model

    • BA Model with fitness

    • BA Model with dynamic fitness

  • Iterated models in this study

    • Improved Model Four (Model I)

    • Constant user energy (Model II)

    • Dynamic user energy (Model III)

Process iii computer simulation3
Process III: Computer Simulation

  • Model I

    • Realistic stochastic procedures.

      • New developer every time step based on Poisson distribution

      • Initial fitness based on log-normal distribution

    • Updated procedure for the weighted project pool (for preferential selection of projects).

Process iii computer simulation6
Process III: Computer Simulation

  • Betweenness and Closeness

Process iii computer simulation7
Process III: Computer Simulation

  • Degree Distributions

Process iii computer simulation8
Process III: Computer Simulation

  • Deficit in the measures

Process iii computer simulation9
Process III: Computer Simulation

  • Model II

    • New addition: user energy.

    • User energy

      • the “fitness” parameter for the user

      • Every time a new user is created, a energy level is randomly generated for the user

      • Energy level will be used to decide whether a user will take a action or not during every time step.

Process iii computer simulation10
Process III: Computer Simulation

  • Degree distributions for Model II

Process iii computer simulation11
Process III: Computer Simulation

  • Deficit in the measures

Process iii computer simulation12
Process III: Computer Simulation

  • Model III

    • New addition: dynamic user energy.

    • Dynamic user energy

      • Decaying with respect to time

      • Self-adjustable according to the roles the user is taking in various projects.

Process iii computer simulation13
Process III: Computer Simulation

  • Degree distributions (Model III)

Process iii computer simulation15
Process III: Computer Simulation

  • Discoveries in Process III

    • Expanding the network models for modeling evolving complex networks (more parameters)

    • Providing a validated model to simulate the community network at

  • Benefits of these discoveries

    • Expanded network models can benefit other researchers in complex networks.

    • Validated model for can be used to study other OSS communities or similar collaboration networks.

Process iv research collaboratory
Process IV: Research Collaboratory

  • Related Research:

    • G. Chin Jr. and C. Lansing, “The biological sciences collaboratory”, Mathematics and Engineering Techniques in Medicine and Biological Sciences, 2004.

    • L. Koukianakis, “A system for hybrid learning and hybrid psychology”, Cybernetics and Information Technologies, Systems and Applications, 2003.

    • NCBI, FlyBase, Ensembl, VectorBase

Process iv research collaboratory1
Process IV: Research Collaboratory

  • What is Collaboratory?

    • An elaborate collection of data, information, analytical toolkits and communication technologies

    • A new networked organizational form that also includes social processes, collaboration techniques and agreements on norms, principles, value, and rules

Process iv research collaboratory3
Process IV: Research Collaboratory

  • Data tier - schema design

Process iv research collaboratory4
Process IV: Research Collaboratory

  • Data tier - connection pool

Process iv research collaboratory5
Process IV: Research Collaboratory

  • Presentation Tier

    • Various access methods

    • Documentation and references

    • Community support

    • Wiki interface

Process iv research collaboratory6
Process IV: Research Collaboratory

  • Logic Tier

    • Interactive web query system

      • Authorized user can submit query to the back end repository through the web query

      • Results are provided by files with various formats

    • Dynamic web schema browser

      • Authorized user can access the dynamic schema of the repository through the schema browser

Process iv research collaboratory7
Process IV: Research Collaboratory

  • Utilization reports

    • Monthly statistics (June 2006)

      • Total queries submitted: 16,947

      • Total data files retrieved: 13,343

      • Total bytes of query data downloaded: 26,684,556,278

  • Programmable access method

    • Programmable access method should be provided for complicated access

    • Web services planned

Process iv research collaboratory8
Process IV: Research Collaboratory

  • Results in Process IV

    • Designing, implementing and maintaining a research collaboratory for OSS related research.

  • Benefits of these results

    • OSS researchers can access one of the most complete data sets for a OSS community development.

    • By providing the community service to OSS researchers, the collaboratory can help in sparkling, improving and promoting research ideas about OSS.


  • Designed and demonstrated a computational discovery methodology to study evolving complex networks using research on OSS as a representative problem domain

  • Understanding the OSS movement by applying the methods.

    • Process I: data mining

      • Identifying significant features to describe a project

      • Using distribution based clustering to generate a distribution based predictor to predict the “popularity” of a project

    • Process II: network analysis

      • Introducing more complete analysis to inspect more complete data set from

      • Discovering high connectivity and possible life cycle behaviors in both the network structure and individuals in the network

    • Process III: computer simulation

      • Introducing more parameters in modeling evolving complex networks

      • Generating a “fit” model to replicate the evolution of the community.

    • Process IV: research collaboratory

      • Designing, implementing and maintaining a research collaboratory to host the data set and provide community support for OSS related researches.

Publications to date
Publications to-date

  • Y. Gao; G. Madey and V. Freeh. “Modeling and simulation of the open source software community”, ADSC, San Diego, 2005.

  • Y. Gao and G. Madey. “Project development analysis of the oss community using st mining”, NAACSOS, Notre Dame, 2005.

  • S. Christley; Y. Gao; J: Xu and G. Madey. “Public goods theory of the open source software development community”, Agent, Chicago, 2004.

  • Y. Gao, Y. Huang and G. Madey, “Data Mining Project History in Open Source Software Communities”, NAACSOS, Pittsburgh, 2004.

  • J. Xu, Y. Gao, J. Goett and G. Madey, “A Multi-model Docking Experiment of Dynamic Social Network Simulations”, Agent, Chicago, 2003.

  • Y. Gao, V. Freeh, and G. Madey, “Analysis and Modeling of the Open Source Software Community”, NAACSOS, Pittsburgh, 2003.

  • Y. Gao, V. Freeh, and G. Madey, “Conceptual Framework for Agent-based Modeling and Simulation”, NAACSOS, Pittsburgh, 2003.

  • G. Madey; V. Freeh; R: Tynan and Y. Gao. “Agent-based modeling and simulation of collaborative social networks”, AMCIS, Tampa, 2003.

  • Y. Gao; V. Freeh and G. Madey. “Topology and evolution of the open source software community”, SwarmFest, Notre Dame, 2003.

Publication plan
Publication Plan

  • Chapter III (data mining)

    • Journal of Machine Learning Research

    • Journal of Systems and Software

  • Chapter IV (network analysis)

    • Journal of Network and Systems Management

    • Journal of Social Structure

  • Chapter V (computer simulation)

    • Spring Simulation Conference 2007 (under review)

    • IEEE Computing in Science and Engineering

  • Chapter VI (research collaboratory)

    • CITSA 2007

    • Journal of Computer Science and Applications

Conclusion and future work
Conclusion and Future Work

  • Cyclic computational discovery method for studying evolving complex networks

  • Study of Open Source Software by applying this method

  • Future works:

    • Maintaining and expanding the collaboratory

    • Verifying the discoveries in the against further accumulated database dump from

    • Applying our simulation model on other software development communities

    • Extending our methodology to other evolving complex networks like Internet, communication network and various social networks


  • My advisor: Dr. Madey

  • My committee members:

    • Dr. Flynn

    • Dr. Striegel

    • Dr. Wood

  • My Colleagues:

    • Scott Christley, Yingping Huang, Tim Schoenharl, Matt Van Antwerp, Ryan Kennedy, Alec Pawling and Jin Xu

  • managers:

    • Jeff Bates, VP of OSTG Inc.

    • Jay Seirmarco, GM of

  • US NSF CISE/IIS-Digital Society & Technology, under Grant No. 0222829.

Case study ii







7597 dev[46]

6882 dev[47]




7597 dev[46]

7597 dev[46]




7597 dev[46]


6882 dev[47]




7597 dev[46]

7028 dev[46]


7597 dev[46]

7028 dev[46]





7597 dev[46]

7028 dev[46]

6882 dev[47]

6882 dev[58]






7597 dev[46]



9859 dev[46]


15850 dev[46]


9859 dev[46]



9859 dev[46]



9859 dev[46]

15850 dev[46]



15850 dev[46]


15850 dev[46]






Case Study II

OSS Developer Network (Part)

Project 7597

Developers are nodes / Projects are links

24 Developers


5 Projects

2 hub Developers

Project 6882

1 Cluster

Project 7028





Project 9859

Project 15850

Process i data mining9
Process I: Data Mining

  • Characteristics of data set

    • Massive

    • Incomplete, noisy, redundant

    • Complex structures, unstructured

  • Classic analysis tools are often inadequate and inefficient for analyzing these data, especially in exploratory research

  • What is DM (Data mining)

    • Nontrivial extraction of implicit, previously unknown and potentially useful information from data.

Process i data mining10
Process I: Data Mining

  • Feature Selection

    • Given a non-negative n x m matrix V, find factors W (n, r) and H (r, m) , such that

      V ≈ W *H

    • This is called the non-negative matrix factorization (NMF) of the matrix V

    • NMF can be used on multivariate data to reduce the dimension of the data set

    • By using NMF, we can reduce dimension from m features to r features

Why nmf
Why NMF?

  • Feature extraction methods

    • linear methods are simpler and more completely understood.

    • nonlinear methods are more general and more difficult to analyze.

  • Linear methods:

    • ICA: Independent Component Analysis

    • Matrix decomposition: PCA, SVD, NMF

  • In practice, NMF is most popular and simple.

  • Dimensionality reduction is effective if the loss of information due to mapping to a lower-dimensional space is less than the gain due simplifying the problem.

Process i data mining11
Process I: Data Mining

  • Feature-based Clustering

    • Grouping data into K number of clusters based on features.

    • The distance metrics used is Euclidean distance like

    • Hierarchical K-Means is used.

      • The result is a binary tree.

      • The root is the whole data set and the leaf clusters are the fine-grained clusters, which are the resulting K clusters.

Process i data mining12
Process I: Data Mining

  • Case Study Result II

  • Unsupervised learning

    • K-Means method used to cluster the project history using the features we selected

    • We named the clusters using ID and the results are shown in the table

    • The result is not acceptable by evaluation

Clustering result evaluation
Clustering Result Evaluation

  • Evaluation test set generation

    • Popular/unpopular projects

    • Stratified sampling to make 500 projects

  • Feature sets used

    • Popular feature set

    • Activity Feature set (Page 34, Table 3.2)

    • Network Feature set (Page35, Table 3.3)

  • Generating rules for the test sets

  • Calculating the support and confidence value

Why k mean

  • The algorithm has remained extremely popular because it converges extremely quickly in practice. In fact, many have observed that the number of iterations is typically much less than the number of points.

  • K-Means is most successful algorithm in large data set (size>1000, dimension > 2) than GA and Evolution

  • CLIQUE is sensitive to noise

  • CURE is not scalable O(n2logn)

  • CLARANS & BIRCH are not good for high dimension data

  • D. Arthur, S. Vassilvitskii (2006): "How Slow is the k-means Method?," Proceedings of the 2006 Symposium on Computational Geometry (SoCG).

K mean

  • It maximizes inter-cluster (or minimizes intra-cluster) variance, but does not ensure that the result has a global minimum of variance. Multiple run is needed.

  • Elbow criterion

Process iii computer simulation16
Process III: Computer Simulation

Simulation model procedure

Process iii computer simulation17
Process III: Computer Simulation

  • Poisson Process:

    • It expresses the probability of a number of events occurring in a fixed period of time if these events occur with a known average rate, and are independent of the time since the last event.

    • PDF:

Process iii computer simulation18
Process III: Computer Simulation

  • Log-normal distribution:

Process iii computer simulation19
Process III: Computer Simulation

  • Kolmogorov-Smirnov test

    • Used to determine whether two underlying one-dimensional distributions differ.

    • Two one-sided K-S test statistics are given by

Similar publications
Similar Publications

  • Chapter III (data mining)

    • JMLR: G. Hamerly, E. Perelman..Using machine learning to guide simulation (Feb. 2006)

    • JSS: S. Kim, J. Yoon..Shape-based retrieval in time-series database (Feb. 2006)

  • Chapter IV (network analysis)

    • JNSM: Special Issue Self-Managing Systems and Networks

    • JoSS: The Journal of Social Structure (JoSS) is an electronic journal of the International Network for Social Network Analysis (INSNA)

  • Chapter V (computer simulation)

    • SSC 2007: simulation co

    • IEEE/CSE: E. Luijten..Fluid simulation with monte carlo algorithm (2006 Vol. 8, Issue 2)

  • Chapter VI (research collaboratory)

    • CITSA 2007: L. Koukianakis..A system for hybrid learning and hybrid psychology (2005)

    • JCSA: S. Chen, K. Wen..An Integrated System for Cancer-Related Genes Mining from Biomedical Literatures (2006)