This document discusses how temporal network analysis can help explore interrelationships in online production systems. It summarizes a talk given by Dr. Claudia Müller-Birn on exploring existing interrelationships in online production systems using temporal network analysis. The talk outlines dimensions of online production systems, issues in modeling and measuring their evolution, influence between social and technical dimensions, and how success can be defined for different online production contexts.
SP1: Exploratory Network Analysis with GephiJohn Breslin
ICWSM 2011 Tutorial
Sebastien Heymann and Julian Bilcke
Gephi is an interactive visualization and exploration software for all kinds of networks and relational data: online social networks, emails, communication and financial networks, but also semantic networks, inter-organizational networks and more. Designed to make data navigation and manipulation easy, it aims to fulfill the complete chain from data importing to aesthetics refinements and interaction. Users interact with the visualization and manipulate structures, shapes and colors to reveal hidden properties. The goal is to help data analysts to make hypotheses, intuitively discover patterns or errors in large data collections.
In this tutorial we will provide a hands-on demonstration of the essential functionalities of Gephi, based on a real case scenario: the exploration of student networks from the "Facebook100" dataset (Social Structure of Facebook Networks, Amanda L. Traud et al, 2011). The participants will be guided step by step through the complete chain of representation, manipulation, layout, analysis and aesthetics refinements. Particular focus will be put on filters and metrics for the creation of their first visualizations. They will be incited to compare the hypotheses suggested by their own exploration to the results actually published in the academic paper afterwards. They finally will walk away with the practical knowledge enabling them to use Gephi for their own projects. The tutorial is intended for professionals, researchers and graduates who wish to learn how playing during a network exploration can speed up their studies.
Sébastien Heymann is a Ph.D. Candidate in Computer Science at Université Pierre et Marie Curie, France. His research at the ComplexNetworks team focuses on the dynamics of realworld networks. He leads the Gephi project since 2008, and is the administrator of the Gephi Consortium.
Julian Bilcke is a Software Engineer at ISC-PIF (Complex Systems Institute of Paris, France). He is a founder and a developer for the Gephi project since 2008.
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
SP1: Exploratory Network Analysis with GephiJohn Breslin
ICWSM 2011 Tutorial
Sebastien Heymann and Julian Bilcke
Gephi is an interactive visualization and exploration software for all kinds of networks and relational data: online social networks, emails, communication and financial networks, but also semantic networks, inter-organizational networks and more. Designed to make data navigation and manipulation easy, it aims to fulfill the complete chain from data importing to aesthetics refinements and interaction. Users interact with the visualization and manipulate structures, shapes and colors to reveal hidden properties. The goal is to help data analysts to make hypotheses, intuitively discover patterns or errors in large data collections.
In this tutorial we will provide a hands-on demonstration of the essential functionalities of Gephi, based on a real case scenario: the exploration of student networks from the "Facebook100" dataset (Social Structure of Facebook Networks, Amanda L. Traud et al, 2011). The participants will be guided step by step through the complete chain of representation, manipulation, layout, analysis and aesthetics refinements. Particular focus will be put on filters and metrics for the creation of their first visualizations. They will be incited to compare the hypotheses suggested by their own exploration to the results actually published in the academic paper afterwards. They finally will walk away with the practical knowledge enabling them to use Gephi for their own projects. The tutorial is intended for professionals, researchers and graduates who wish to learn how playing during a network exploration can speed up their studies.
Sébastien Heymann is a Ph.D. Candidate in Computer Science at Université Pierre et Marie Curie, France. His research at the ComplexNetworks team focuses on the dynamics of realworld networks. He leads the Gephi project since 2008, and is the administrator of the Gephi Consortium.
Julian Bilcke is a Software Engineer at ISC-PIF (Complex Systems Institute of Paris, France). He is a founder and a developer for the Gephi project since 2008.
The presentation is a introduction to Big Data and analytics, how to go about enabling big data and analytics in our company, what are the main differences between big data analytics vs. traditional analytics and how to get started.
This material was used at the SAS Big Data Analytics event held in Helsinki on 19th of April 2011.
The slides are copyright of Accenture.
Edinburgh Data-Intensive Research Data-intensive refers to huge volumes of data, complex patterns of data integration and analysis, and intricate interactions between data and users. Current methods and tools are failing to address data-intensive challenges effectively. They fail for several reasons, all of which are aspects of scalability. The deluge of computational methods and plethora of computational systems prevents effective and efficient use of resources, user interfaces are not adopted at a sufficient rate to satisfy demand for scientific computing and data and knowledge is created outside suitable contexts for collaborative research to be effective. The Edinburgh Data-Intensive Research group addresses these scalability issues by providing mappings from abstract formulations to concrete and optimised executions of research challenges, by developing intuitive interfaces to enable access to steer these executions and by developing systems to aid in creating new research challenges. In this talk I will present several exemplars where we have dealt with scalability issues in scientific scenarios.
Using social network analysis, complex intangible relationship patterns can reveal competitive forces, gatekeepers and collaboration opportunities - within and across sectors - in internal and external innovation ecosystems around the world, including China 2.0.
A Western View of China's Internal and External Innovation Ecosystem - ICT Se...Martha Russell
A network analysis of flows of information and investments a relationship perspective on the internal and external innovation ecosystems of China's ICT sectors. Crowd-sourced English language press release-type information provides a Western view in a systems framework.
From Web Data to Knowledge: on the Complementarity of Human and Artificial In...Stefan Dietze
Inaugural lecture at Heinrich-Heine-University Düsseldorf on 28 May 2019.
Abstract:
When searching the Web for information, human knowledge and artificial intelligence are in constant interplay. On the one hand, human online interactions such as click streams, crowd-sourced knowledge graphs, semi-structured web markup or distributional semantic models built from billions of Web documents are informing machine learning and information retrieval models, for instance, as part of the Google search engine. On the other hand, the very same search engines help users in finding relevant documents, facts, or data for particular information needs, thereby helping users to gain knowledge. This talk will give an overview of recent work in both of the aforementioned areas. This includes 1) research on mining structured knowledge graphs of factual knowledge, claims and opinions from heterogeneous Web documents as well as 2) recent work in the field of interactive information retrieval, where supervised models are trained to predict the knowledge (gain) of users during Web search sessions in order to personalise rankings. Both streams of research are converging as part of online platforms and applications to facilitate access to data(sets), information and knowledge.
This talk presents areas of investigation underway at the Rensselaer Institute for Data Exploration and Applications. First presented at Flipkart, Bangalore India, 3/2015.
Human-Machine Collaboration in Networked Information SystemsMüller-Birn Claudia
Over the past decade, computers and networks have become increasingly ubiquitous. They have a central role in how we work, communicate, and learn. In this talk, I introduce the concept of human-machine collaboration and show how networked information systems enable coupled relationships between humans and machines. I will take four perspectives on this coupled relationships: how we can design, analyze, develop and also extend them. The presented approaches contribute to the research field of networked socio-technical information systems. By unfolding design parameters of these systems, we can develop adaptive networked information systems that, in the future, can reduce the friction between humans and machines to a point where they become a natural extension of our human experience.
Edinburgh Data-Intensive Research Data-intensive refers to huge volumes of data, complex patterns of data integration and analysis, and intricate interactions between data and users. Current methods and tools are failing to address data-intensive challenges effectively. They fail for several reasons, all of which are aspects of scalability. The deluge of computational methods and plethora of computational systems prevents effective and efficient use of resources, user interfaces are not adopted at a sufficient rate to satisfy demand for scientific computing and data and knowledge is created outside suitable contexts for collaborative research to be effective. The Edinburgh Data-Intensive Research group addresses these scalability issues by providing mappings from abstract formulations to concrete and optimised executions of research challenges, by developing intuitive interfaces to enable access to steer these executions and by developing systems to aid in creating new research challenges. In this talk I will present several exemplars where we have dealt with scalability issues in scientific scenarios.
Using social network analysis, complex intangible relationship patterns can reveal competitive forces, gatekeepers and collaboration opportunities - within and across sectors - in internal and external innovation ecosystems around the world, including China 2.0.
A Western View of China's Internal and External Innovation Ecosystem - ICT Se...Martha Russell
A network analysis of flows of information and investments a relationship perspective on the internal and external innovation ecosystems of China's ICT sectors. Crowd-sourced English language press release-type information provides a Western view in a systems framework.
From Web Data to Knowledge: on the Complementarity of Human and Artificial In...Stefan Dietze
Inaugural lecture at Heinrich-Heine-University Düsseldorf on 28 May 2019.
Abstract:
When searching the Web for information, human knowledge and artificial intelligence are in constant interplay. On the one hand, human online interactions such as click streams, crowd-sourced knowledge graphs, semi-structured web markup or distributional semantic models built from billions of Web documents are informing machine learning and information retrieval models, for instance, as part of the Google search engine. On the other hand, the very same search engines help users in finding relevant documents, facts, or data for particular information needs, thereby helping users to gain knowledge. This talk will give an overview of recent work in both of the aforementioned areas. This includes 1) research on mining structured knowledge graphs of factual knowledge, claims and opinions from heterogeneous Web documents as well as 2) recent work in the field of interactive information retrieval, where supervised models are trained to predict the knowledge (gain) of users during Web search sessions in order to personalise rankings. Both streams of research are converging as part of online platforms and applications to facilitate access to data(sets), information and knowledge.
This talk presents areas of investigation underway at the Rensselaer Institute for Data Exploration and Applications. First presented at Flipkart, Bangalore India, 3/2015.
Human-Machine Collaboration in Networked Information SystemsMüller-Birn Claudia
Over the past decade, computers and networks have become increasingly ubiquitous. They have a central role in how we work, communicate, and learn. In this talk, I introduce the concept of human-machine collaboration and show how networked information systems enable coupled relationships between humans and machines. I will take four perspectives on this coupled relationships: how we can design, analyze, develop and also extend them. The presented approaches contribute to the research field of networked socio-technical information systems. By unfolding design parameters of these systems, we can develop adaptive networked information systems that, in the future, can reduce the friction between humans and machines to a point where they become a natural extension of our human experience.
Mädchenorientiertes Interaktionsdesign – Fallstricke und ChancenMüller-Birn Claudia
Die Anzahl der Schülerinnen, die sich für ein MINT-Studium interessieren und dieses erfolg- reich absolvieren, konnte in den vergangenen Jahren nicht signifikant gesteigert werden. Eine Initiative, die seit Jahren versucht, diesem Trend der nach wie vor stark geschlechtsspezifischen Studienfach- und Berufswahl entgegenzuwirken, ist der Girls’ Day. Seit 2002 wird dieser Tag auch an der FU Berlin veranstaltet. In diesem anwendungsorientierten Beitrag werden wir das von uns gewählte Vorgehen zur Entwicklung einer neuen Webanwendung für Mädchen vorstellen und diskutieren.
What and how users read: Transforming reading behavior into valuable feedback...Müller-Birn Claudia
Most of the attention in previous research on the Wikipedia community has been devoted to the study of its production side: editors and their motivations, activity and roles. However, the value of the encyclopedia is also given by the millions of people who access it every day. In this work we focus on the - until now understudied - usage side of Wikipedia, investigating readers’ preference and behaviour as a precious source of information that can provide useful feedback to the editors’ community.
More information here: https://wikimania2014.wikimedia.org/wiki/Submissions/Who_reads_what_and_how:_Transforming_reading_behavior_into_valuable_feedback_for_the_Wikipedia_community
Work-to-Rule: The Emergence of Algorithmic Governance in WikipediaMüller-Birn Claudia
Research has shown the importance of a functioning governance system for the success of peer production communities. It particu- larly highlights the role of human coordination and communication within the governance regime. In this article, we extend this line of research by differentiating two categories of governance mech- anisms. The first category is based primarily on communication, in which social norms emerge that are often formalized by written rules and guidelines. The second category refers to the technical infrastructure that enables users to access artifacts, and that allows the community to communicate and coordinate their collective ac- tions to create those artifacts. We collected qualitative and quan- titative data from Wikipedia in order to show how a community’s consensus gradually converts social mechanisms into algorithmic mechanisms. In detail, we analyze algorithmic governance mech- anisms in two embedded cases: the software extension “flagged revisions” and the bot “xqbot”. Our insights point towards a grow- ing relevance of algorithmic governance in the realm of governing large-scale peer production communities. This extends previous re- search, in which algorithmic governance is almost absent. Further research is needed to unfold, understand, and also modify exist- ing interdependencies between social and algorithmic governance mechanisms.
Visual Analytics in der Schule - Mit Daten lehren, aus Daten lernenMüller-Birn Claudia
Das Ziel des Visual Analytics ist es, Werkzeuge und Techniken zur Visualisierung großer, dynamischer und oft unvollständiger Datensätze bereitzustellen. Durch die Interpretation der Visualisierung können Informationen abgeleitet werden, die dazu dienen, erwartete und unerwartete Erkenntnisse zu gewinnen. Erst durch die Kombination dieser Erkenntnisse mit bestehenden Erfahrungen, kann beim Betrachter Wissen über vorhandene Zusammenhänge generiert werden. Was aber genau verbirgt sich hinter diesem Prozess und wie kann er im Kontext der schulischen Ausbildung genutzt werden? Es werden Möglichkeiten vorgestellt und diskutiert wie mit Hilfe des Visual Analytics Schülerinnen und Schüler für die Informatik und deren Möglichkeiten interessiert werden können.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
2024.06.01 Introducing a competency framework for languag learning materials ...
How temporal network analysis can help us to explore existing interrelationships in online production systems
1. How temporal network analysis can help us to explore
existing interrelationships in online production systems
Dr. Claudia Müller-Birn
Institute for Computer Science, Group Networked Information Systems
January 20, 2011
Invited Talk, GESIS, Bonn
2. When you think of the Social Web...
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 2
Claudia Müller-Birn
3. Social participation creates digital products
STEM (Spatio-Temporal
Exploratory Model) map Can Distributed Volunteers Accomplish
of Dickcissel (http:// Massive Data Analysis Tasks?
ebird.org) (Kanefsky et al., 2001)
Graph of source
lines of code
added [millions]
(Deshpande &
Riehle, 2008)
dataset based on
www.ohloh.net
Number of articles on English-language
Wikipedia from its creation in 2001
through June 2010 (Riedl, 2011)
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 3
Claudia Müller-Birn
4. Social participation creates digital products
• Geographically distributed communities
STEM (Spatio-Temporal
Exploratory Model) map Can Distributed Volunteers Accomplish
• Very large number of granular, individual contributions Tasks?
of Dickcissel (http://
Massive Data Analysis
ebird.org)
(Kanefsky et al., 2001)
• Openness of boundaries, technical standards,
communication and information sources
• Peering as a new form of horizontal organization
• Sharing of intellectual property
Graph of source
(Benkler, 2006), (OMahony, 2007), (Tapscott2007)
lines of code
added [millions]
(Deshpande &
Riehle, 2008)
dataset based on
www.ohloh.net
Number of articles on English-language
Wikipedia from its creation in 2001
through June 2010 (Riedl, 2011)
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 3
Claudia Müller-Birn
5. Outline
• Dimensions in online production systems and existing research issues
• Success in online production systems
• Mirroring hypothesis in online production systems
(research in progress)
• Recent and future research challenges
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 4
Claudia Müller-Birn
6. Dimensions in online production systems
pooled structured integral
product product product
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 5
Claudia Müller-Birn
7. Selected research issues in online production systems
MODELING QUALITY/SUCCESS
• How do we model the • How do we measure quality or
dimensions of online success?
production systems? • How do online production
• Which network systems strive for quality?
descriptions are
especially useful?
• What are appropriate
data sources?
EVOLUTION
• How do the social and
the technical dimension INFLUENCE
co-evolve? • How do we measure the
• What techniques can be influence of the technical
used for measuring and dimension on the social dimension
describing evolution? and vice versa?
• Are specific structures of networks
more influential than others?
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 6
Claudia Müller-Birn
8. Success in online production systems:
A longitudinal analysis of the socio-technical duality of
development projects*
Müller-Birn, C., Cataldo, M., Wagstrom, P., Herbsleb, J.D.: Success in Online Production
Systems: A Longitudinal Analysis of the Socio-Technical Duality of Development Projects.
Technical Report CMU-ISR-10-129, 2010.
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 7
Claudia Müller-Birn
9. What might be success factors for OPSs?
Success of virtual community sites (Preece, 2000):
Usability: human-technology interactions (e.g., information design, navigation, and access)
Sociability: human-human interactions by developing policies and practices that are socially
acceptable and practicable
Success drivers are number of In Wikipedia the success of an article can
participants who communicate, the be seen as its quality (Kittur & Kraut,
number of exchanged messages, 2008) (there are certain requirements in
interactivity, and reciprocity (Preece, order to get assigned into a six-level
2001) quality system, ranging from
“stub” (almost no content) to “featured-
article” (best quality))
In product development,
conceptualizations such as market
performance of the product,
project cycle time, efficiency of In open source projects typically
the development process and quantifications of volume related to number
product quality are used (Clark & of contributors or participants or
Fujimoto, 1990), (Eisenhardt & number of access to the particular
Tabrizi, 1995), (Sethi, 2000) project’s product or outcome (Crowston et
al., 2006), (Iriberri & Leroy, 2009) is used
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 8
Claudia Müller-Birn
10. Open source software (OSS) project GNOME
• Graphical user interface and a development framework for desktop
applications
• GNOME is a large collection of libraries and applications rather than a
monolith application (German, 2003)
• Data covered a period of about 8 years of
activity from November 1997 until July 2005 Description Value
Mail repository
Number of emails 467,639
Number of senders 34,662
Date of first email 01-01-1997
Date of last email 02-10-2007
Code repository
Number of committer 1,312
Number of commits 479,678
Number of files 286,314
Number of commits (files) 2,456,302
Date of first commit 12-22-1996
Date of last commit 08-01-2005
Bug repository
Number of users 2,706
Number of bugs 201,068
Date of first bug 01-01-1999
Date of last bug 11-18-2005
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 9
Claudia Müller-Birn
11. How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 10
Claudia Müller-Birn
12. Used data set
• Community hosted over 700 different projects
• Projects differ significantly in their development activity, size, and
participation rate
• Projects were included if they satisfy all of the following criteria
- Continuity of development activity (at least one year)
- Amount of development activity (at least 100 commits)
- Attractiveness of project for developers (at least 10 committers),
- User interest to participate (at least one community hosted mailing list)
- Data collected from different repositories should overlap during the
analyzed period
• Further used data set consists of 27 projects
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 11
Claudia Müller-Birn
13. Social Dimension
• Coordination needs network
- Computation of coordination needs networks for each project by computing
(Task Assignment ∗ Task Dependency) ∗ Transpose(Task Assignment)
(Cataldo et al., 2008)
- Task assignment: which individuals are working on which tasks
- Task dependency: relationships or dependencies among tasks
• Communication network
- Construction of a collection of communication networks for each project
from the project’s mailing list
- Construction of communication networks of the whole OPS by aggregating
the project-level communication networks into one
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 12
Claudia Müller-Birn
14. Technical Dimension
• Syntactic Dependency Network
- Examination of source code and extracting data-related dependency (e.g.,
a particular data structure modified by a function and used in another
function) and functional dependency (e.g., method A calls method B)
relationships between source code files during the period of time between
two releases of the GNOME distribution
• Logical Dependency Network
- Construction of the logical dependencies network by extracting the set of
source code files that were modified as part of development tasks
performed during the period of time between two releases of the GNOME
distribution
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 13
Claudia Müller-Birn
15. Results
• Successful projects benefit from interaction patterns that are able to
disseminate information to most of the project participants
while minimizing redundant interconnections
• Successful projects exhibit a continuously active core group that
is able to integrate all member of the project or the developed
software
• Project success depends on its members occupying different
structural positions within the network as a mechanism to balance
the benefits and limitations of belonging solely to the core or the
periphery
• When tasks dependencies are partitioned among separate
clusters of highly interdependent sets of individuals, projects are
more likely to succeed
• Modular technical structures (those with independent clusters of
highly interdependent parts) are an important success driver for
online production systems
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 14
Claudia Müller-Birn
16. Mirroring hypothesis in online production systems
using temporal network analysis (research in progress)
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 15
Claudia Müller-Birn
17. Co-evolution of social and technical architectures
• Social architecture should reflect the technical architecture of a
system and vice versa in order to improve the degree of innovation
or to reduce the coordination needs
(Conway, 1968), (Baldwin & Clark, 2000), (Cataldo et al., 2008)
• Open collaborative communities are geographically distributed;
therefore, their technical architecture should be modular (e.g., (Moon &
Sproull, 2000))
• In the context of OSS, a modular technical architecture increases
incentives to join and decreases free riding (Baldwin & Clark, 2006), (West &
Mahony, 2008)
• BUT recent empirical work has shown that this hypothesis can only
be partly supported in open collaborative settings (Colfer & Baldwin 2010)
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 16
Claudia Müller-Birn
18. Requirements for model description
• Networks are used to describe communities therefore the relation
between the people (density of links) should be used as measure
• Evolution of networks over time; therefore, a temporal model is
required
• Large membership base in open collaborative communities
therefore the algorithm should be able to deal with large networks
• Complete knowledge about the networks is often not available
therefore the algorithm should detect local communities
• People are often actively involved in different communities;
therefore, the algorithm should allow overlapping communities
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 17
Claudia Müller-Birn
19. Brief overview on existing approaches
• Discrete approach to consider time in graphs (Moody, 2005)
- Cross-sectional analysis of graphs where the main focus lies on the changes
of network stages (e.g., (Cortes, 2003), (Sun, 2007))
- Approaches to discretize the interactions (a) the cumulative approach and
(b) the time window approach
• Continuous approach to consider time in graphs (Moody, 2005)
- Each single interaction with a start and end date is considered (e.g.,
(Kumar, 2003r), (Priebe, 2005))
• Describing evolution in networks based on a group-level
- Network quality function (Mucha et al. 2010)
- Dynamic tensor analysis (Sun et al. 2006)
- Evolutionary spectral clustering (Chi et al. 2007)
- Clique percolation method (Palla et al., 2005)
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 18
Claudia Müller-Birn
20. Experimental setup
• Data set: OOS project Epiphany (web browser)
• Communication network based on mailing list repository
• One time frame considers three months of activity
• Steps of CPM Description Value
- Locate all complete subgraphs, i.e. cliques, # month 44
that are not part of a larger subgraph # senders 688
# mails 8,352
- Identify communities based on # threads 1,294
clique-clique overlap matrix # committers 208
- Specify “optimal” percolation structure # commits 5,898
# files 21,223
# added LOC 957,091
# removed LOC 748,956
mails per person 12.00
persons per thread 6.45
commits per person 28.36
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 19
Claudia Müller-Birn
21. Selected community and network characteristics
10,000 0.003 9
edges
8
nodes 0.0025
1,000 7
!"#$%&'()'!(*%+,%*-%+'
0.002 6
!"#$%&%'(
5
!"#$%&'(
100 0.0015
4
0.001 3
10
2
0.0005
1
1 0 0
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
$#)*$+,&(
+!./+0(1' )*#+),-&(
90.00% 90
k=3 not included k=3
80.00% 80
k=4 not included k=4
k=5 not included
percentage of non-included nodes
70.00% 70 k=5
!"#$%&'%()*+$!,%-&../0",1%
60.00% 60
50.00% 50
40.00% 40
30.00% 30
20.00% 20
10.00% 10
0.00% 0
!" #" $" %" &" '" (" )" *" !+" 1 2 3 4 5 6 7 8 9 10
snapshot !0)2!3&,%
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 20
Claudia Müller-Birn
22. Community development based on social interactions
90
new (leaving)
80 new
old (leaving)
70 old
60
50
size
40
30
20
10
0
1 2 3 4 5 6 7 8 9 10
snapshot
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 21
Claudia Müller-Birn
23. Recent and future research challenges
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 22
Claudia Müller-Birn
24. Conclusions
• Considering time by describing the two dimensions helps to reveal
dependencies between development patterns and the specific life
cycle stage of an OPS
• Success of an online production system is related to the social AND
technical dimension; thus, describing both dimensions is a
requirement to understand and to improve existing production
processes
• Other research has shown that organizational and technical
structures are related; necessity to explore existing
interdependencies in OPSs
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 23
Claudia Müller-Birn
25. Thank you.
Acknowledgements
Co-authors: Marcelo Cataldo, James D. Herbsleb
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 24
Claudia Müller-Birn
26. References
• C.Y. Baldwin and K.B. Clark: Design Rules: The Power of Modularity Volume 1. MIT Press, Cambridge, MA, USA, 1999.
• C.Y. Baldwin and K.B. Clark. The Architecture of Participation: Does Code Architecture Mitigate Free Riding in the Open
Source Development Model? Management Science. 52:7. 2006.
• Benkler, Y., & Nissenbaum, H. Commons based Peer Production and Virtue*. Journal of Political Philosophy, 14(4): 394-419.
2006.
• M. Cataldo, J.D. Herbsleb, K.M. Carley. Socio-technical congruence: a framework for assessing the impact of technical and
work dependencies on software development productivity, Proceedings of the Second ACM-IEEE international symposium on
Empirical software engineering and measurement: 2-11. Kaiserslautern, Germany: ACM. 2008.
• Y. Chi, S. Zhu, X. Song, J. Tatemura and B.L. Tseng. Structural and temporal analysis of the blogosphere through
community factorization. KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery
and data mining. ACM. San Jose, California, USA, 163-172. 2007.
• K. Clark and T. Fujimoto. Product Development Performance. Harvard Business School Press, 1991.
• M. E. Conway. How do Committees Invent? Datamation. 14:4. 28-31. 1968.
• C. Cortes, D. Pregibon and C. Volinsky: Computational Methods for Dynamic Graphs. Journal of Computational and
Graphical Statistics. 12:4. 950-970. 2003.
• K. Crowston, J. Howison, H. Annabi, H. Information systems success in free and open source software development: theory
and measures. Software Process: Improvement and Practice, 11(2): 123-148. 2006.
• A. Deshpande and D. Riehle: The Total Growth of Open Source. Proceedings of the Fourth Conference on Open Source
Systems (OSS 2008). Springer Verlag. 197-209. 2008.
• K. Eisenhardt and B. Tabrizi. Accelerating adaptive processes: Product innovation in the global industry. Administrative
Science Quarterly, 40(1):84–110, 1995.
• A. Iriberri and G. Leroy. A life-cycle perspective on online community success. ACM Comput. Surv., 41(2):1–29, 2009.
• A. Kittur and R. E. Kraut. Harnessing the wisdom of crowds in wikipedia: quality through coordination. In Proc. of CSCW,
pages 37–46, 2008.
• B. Kanefsky, N.G. Barlow, V.C. Gulick. Can Distributed Volunteers Accomplish Massive Data Analysis Tasks?. 32nd Annual
Lunar and Planetary Science Conference. 2001.
• R. Kumar, J. Novak, P. Raghavan, and A. Tomkins. On the bursty evolution of blogspace. WWW '03: Proceedings of the 12th
international conference on World Wide Web. ACM, New York, NY, USA. 568--576. 2003.
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 25
Claudia Müller-Birn
27. References (cont.)
• J. Moody, D. McFarland and S. Bender-deMoll. Dynamic Network Visualization. American Journal of Sociology. 110:4.
1206-1241. 2005.
• J.Y. Moon and L. Sproull. Essence of Distributed Work: The Case of the Linux Kernel. First Monday. 5:11. 2000.
• L. Sproull and S. Kiesler. Connections - new ways of working in the networked organization. MIT Press. Cambridge, Mass.
1995.
• P.J. Mucha, T. Richardson, K. Macon, M.A. Porter, J-P. Onnela: Community Structure in Time-Dependent, Multiscale, and
Multiplex Networks. Science. 328: 5980. 876-878. 2010.
• C. Müller-Birn, M. Cataldo, P. Wagstrom, J.D. Herbsleb: Success in Online Production Systems: A Longitudinal Analysis of
the Socio-Technical Duality of Development Projects. Technical Report CMU-ISR-10-129, 2010.
• O'Mahoney, S., & Ferraro, F. The emergence of governance in an open source community. Academy of Management Journal,
50(5): 1079-1106. 2007.
• G. Palla, I. Dereny, I. Farkas, I, T. Vicsek. Uncovering the overlapping community structure of complex networks in nature
and society. Nature. 435: 7043. 814-818. 2005.
• J. Preece. Online Communities: Designing Usability, Supporting Sociability. John Wiley & Son, 2000.
• J. Preece. Sociability and usability in online communities: determining and measuring success. Behav. & Inform. Techn., 20
(5):347–356, 2001.
• C.E. Priebe, J.M. Conroy, D.J. Marchette, and Y. Park. Scan Statistics on Enron Graphs. Computational and Mathematical
Organization Theory Journal. 11:3. 229-247. 2005
• J. Riedl. The Promise and Peril of Social Computing. Computer. 44:1. 93-95. 2011.
• R. Sethi. New product quality and product development teams. Journal of Marketing, 64:1–14, 2000.
• J. Sun, D. Tao and C. Faloutsos. Beyond streams and graphs: dynamic tensor analysis. KDD '06: Proceedings of the 12th
ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. New York, NY, USA. 374-383. 2006.
• J. Sun. Incremental pattern discovery on streams, graphs and tensors (phdthesis). CMU. Pittsburgh, PA, USA. 2007.
• D. Tapscott, A. Williams. Wikinomics: How mass collaboration changes everything: Portfolio Trade. 2008.
• J. West and S. O'Mahony. The Role of Participation Architecture in Growing Sponsored Open Source Communities. Industry
and Innovation. 15:2. 145-168. 2008.
How temporal network analysis can help us to explore existing interrelationships in online production systems. January 20, 2011 26
Claudia Müller-Birn