This document provides an introduction to Stochastic Actor-Oriented Models (SAOMs), also known as SIENA models. It discusses when SAOMs are appropriate to use, provides an overview of the general SAOM form, and covers key components like the network and behavior objective functions and rate functions. The presentation also outlines how SAOMs are estimated and fitted to data, provides an empirical example, and discusses extensions. SAOMs model how networks and behaviors change over time as actors make micro-level decisions to maximize their objective functions.
Stochastic actor-oriented models (SAOMs) summarize the key components and estimation process of these models. SAOMs model how networks and behaviors change over time as a result of endogenous network effects and influence between connected individuals. The models estimate parameters representing these effects to predict tie formation and changes in behaviors. SAOMs account for selection into the network based on attributes as well as social influence processes within the network. Estimation involves maximum likelihood to estimate parameters of network and behavior functions that represent how individuals make network and behavioral decisions.
Stochastic actor-oriented models (SAOMs) allow for the modeling of interdependent network and behavioral change over time. SAOMs conceptualize change as occurring through a sequence of micro-steps, with actors making decisions to change their ties or behaviors based on maximizing an objective function. The objective function specifies how different network and behavioral effects influence these decisions. SAOMs can be used to estimate the effects of network structure on behaviors or behaviors on network structure while accounting for endogenous network and behavioral processes.
This document provides an introduction to Stochastic Actor-Oriented Models (SAOMs), also known as SIENA models. It discusses when SAOMs are appropriate to use, provides an overview of the general SAOM form, and covers key components like the network and behavior objective functions and rate functions. The presentation also outlines how SAOMs are estimated and fitted to data, provides an empirical example, and discusses extensions. SAOMs model how networks and behaviors change over time as actors make micro-level decisions to maximize their objective functions.
ETL Validator Usecase - Checking for DuplicatesDatagaps Inc
ETL Validator gives quick and easy way to create test cases for identifying Duplicates in data sources. Here, we will create a test case that will identify duplicates of First Name + Last Name.
The document describes a student tracking system project that was presented by three students. It includes an abstract, introduction, description of the existing system and proposed system. It then outlines the various modules involved, project requirements, hardware requirements, software requirements, and design aspects including E-R diagrams, data flow diagrams, UML diagrams, screen shots and test cases. The design aspects show how data will flow through the system and how the various objects will interact with each other.
The document summarizes an algorithm for overlapping community detection in networks. It discusses background on community definitions and types, describes four categories of algorithms - clique percolation, link partition, local expansion, and dynamics. For each category it provides examples to illustrate how the algorithms work by extracting communities from sample graphs. It also discusses evaluation metrics used to assess detected communities.
Stochastic actor-oriented models (SAOMs) summarize the key components and estimation process of these models. SAOMs model how networks and behaviors change over time as a result of endogenous network effects and influence between connected individuals. The models estimate parameters representing these effects to predict tie formation and changes in behaviors. SAOMs account for selection into the network based on attributes as well as social influence processes within the network. Estimation involves maximum likelihood to estimate parameters of network and behavior functions that represent how individuals make network and behavioral decisions.
Stochastic actor-oriented models (SAOMs) allow for the modeling of interdependent network and behavioral change over time. SAOMs conceptualize change as occurring through a sequence of micro-steps, with actors making decisions to change their ties or behaviors based on maximizing an objective function. The objective function specifies how different network and behavioral effects influence these decisions. SAOMs can be used to estimate the effects of network structure on behaviors or behaviors on network structure while accounting for endogenous network and behavioral processes.
This document provides an introduction to Stochastic Actor-Oriented Models (SAOMs), also known as SIENA models. It discusses when SAOMs are appropriate to use, provides an overview of the general SAOM form, and covers key components like the network and behavior objective functions and rate functions. The presentation also outlines how SAOMs are estimated and fitted to data, provides an empirical example, and discusses extensions. SAOMs model how networks and behaviors change over time as actors make micro-level decisions to maximize their objective functions.
ETL Validator Usecase - Checking for DuplicatesDatagaps Inc
ETL Validator gives quick and easy way to create test cases for identifying Duplicates in data sources. Here, we will create a test case that will identify duplicates of First Name + Last Name.
The document describes a student tracking system project that was presented by three students. It includes an abstract, introduction, description of the existing system and proposed system. It then outlines the various modules involved, project requirements, hardware requirements, software requirements, and design aspects including E-R diagrams, data flow diagrams, UML diagrams, screen shots and test cases. The design aspects show how data will flow through the system and how the various objects will interact with each other.
The document summarizes an algorithm for overlapping community detection in networks. It discusses background on community definitions and types, describes four categories of algorithms - clique percolation, link partition, local expansion, and dynamics. For each category it provides examples to illustrate how the algorithms work by extracting communities from sample graphs. It also discusses evaluation metrics used to assess detected communities.
Astute Business Solutions - Fast Track Impact Analysis for PeopleSoft 9.2 Upg...Arvind Rajan
Fast Track Impact Analyzer and Accelerated Upgrade for PeopleSoft provides tools and services to assess and accelerate PeopleSoft upgrades. It uses an automated assessment tool to quickly analyze upgrade efforts and produce reliable estimates. This helps clients upgrade more quickly and reduces risks and costs compared to traditional manual approaches. Services include upgrade planning, testing, and support.
The document discusses requirements engineering for a new customer data management system. It describes approaches for identifying business needs, defining internal requirements, and analyzing requirements against external client needs. The key steps outlined include eliciting requirements through workshops, interviews, questionnaires and documentation; analyzing and categorizing requirements; modeling system context and flows; and presenting requirements for validation and change management.
Software Requirements Specification (SRS) - Records Management with Chatbot A...FedericoLibadiaII
The purpose of this document is to build a computerized barangay record management
system to organize the files and to lessen the barangay officials' paper works. They could complete
things more quickly, and a large workforce wouldn't be required. The system's purpose is also to
offer safer storage, quick file retrieval, preserve dependability and correctness, faster file tracking,
and enhance communication quality. The staff finds that offering software to replace manual
record-keeping and the hand-issuing of barangay clearances, certificates, and other barangay
insurances to be of great assistance. This approach also suggests creating a chatbot assistant that
locals can use to rapidly find answers to their inquiries on the barangay website. This chatbot can
initiate polite dialogues, respond to barangay information, address frequently asked questions, and
provide details on the locations, people to contact, and activities of the barrio. Additionally, the
chatbot can aid in enhancing communication between the barangay and its residents. As the
barangay is frequently the subject of questioning from the locals.
A university management system is a software application designed to help universities manage their academic and administrative operations more efficiently. It provides a centralized platform for managing student records, course schedules, faculty information, financial transactions, and other important aspects of university operations.
With a university management system, administrators and faculty members can easily access and update student records, track course schedules and enrollment, and generate reports on academic performance and financial transactions. This system helps to streamline operations and reduce administrative burden, allowing faculty members to focus on teaching and research and students to focus on their studies.
1. The document describes the features of a student health assessment and management system.
2. The system is divided into 3 main sections: administration, data entry and reporting, and report and data analysis.
3. The administration section allows managing student, class, user and assessment cycle data, while the data entry section enables inputting student health assessments.
4. The report section generates various individual and consolidated reports on students' health statuses.
Introduction to Process Mining and its applicability to enterprise technology customer support. Please visit my blog at http://www.haimtoeg.com/?p=1881 for further discussion.
The Rational Unified Process (RUP) is a software engineering process that provides a disciplined approach to development. It aims to ensure high-quality software is produced within budget and on schedule. RUP supports object-oriented techniques and uses the Unified Modeling Language. It consists of inception, elaboration, construction, and transition phases with iterations. Benefits of RUP include better control over software, resolving risks, supporting changes and iterative development.
Crédits d’impôts CICE, CII, CIR, CIJV, CIE & Statut de JEI dans le domaine Mu...Marseille Innovation
Informations sur les dernières mesures fiscales JEI, CIR, CII dans le secteur des Médias
Marseille Innovation vous propose un petit déjeuner d’information sur les dernières mesures annoncées :
Crédit d'Impôt Innovation (CII), Crédit d’Impôt Recherche (CIR), statut de Jeune Entreprise Innovante (JEI),
Les spécificités pour les entreprises du secteur des Médias, contenus, jeux, et applications en ligne
Comment bénéficier de ce dispositif, quels sont les critères d’éligibilité
Informations sur le Crédit d’Impôt Jeu vidéo
En quoi consiste le crédit d'impôt jeux vidéo
Comment bénéficier de ce dispositif, quels sont les critères d’éligibilité
This document contains the resume of Satish Damodhar Pund. It outlines his professional experience as a software tester working on insurance and energy domain projects. It details his technical skills including Java, databases, testing tools and mainframe experience. It provides summaries of his roles and responsibilities on past projects for clients like IBM, Accenture and others. The resume highlights his testing abilities around requirements, test case design, defect logging and tracking, and ensuring quality standards.
A document discusses various software estimation techniques including function point analysis, COCOMO models, and cost drivers. Function point analysis breaks a system into functional components like inputs, outputs, inquiries and files that are assigned complexity weights and counts. COCOMO models like COCOMO I and COCOMO II estimate effort using size of the project and cost multipliers related to attributes of the product, computer system, personnel and project. Cost drivers help assess these multipliers to refine effort estimates.
This document provides an overview of exponential random graph models (ERGMs) for statistically modeling social networks. It discusses the goals of using ERGMs, which are to understand structural features of networks, test hypotheses about network formation processes, and link macro network structures to micro behaviors. Example model terms that can be used in ERGMs are described, ranging from simple models with just edges to more complex models incorporating triangles, degree distributions, and homophily. The document outlines the challenges of estimating ERGM parameters using maximum likelihood due to the normalizing constant, and notes that simulation-based approximations are typically used.
The document discusses network diffusion and peer influence. It covers compartmental models of diffusion, how network structure affects diffusion through factors like distance, clustering, and highly connected nodes. Simulation studies show networks with shorter path distances, more independent paths between nodes, and higher clustering coefficients diffuse ideas and behaviors more quickly. The regression analysis finds these network structural characteristics strongly predict a network's relative diffusion ratio compared to random networks.
This document provides an overview of ego network analysis. It defines ego networks as consisting of a focal individual (ego) and the people they are connected to (alters). Various measures of ego network composition, structure, and properties can be analyzed, such as size, density, and homophily. These measures provide insight into an individual's social support and influence, and can be used to study health-related questions by examining the characteristics and behaviors present in one's social network. Ego network data is relatively easy to collect and can offer information about both individuals and inferred properties of broader social networks.
12 Network Experiments and Interventions: Studying Information Diffusion and ...dnac
This document summarizes research on studying information diffusion and collective action through network experiments and interventions. The research aims to identify optimal strategies for information dissemination for public policy by comparing the effectiveness of different dissemination methods, including using phone/IVR, government representatives, and social network seeds. It also examines how an individual's decision to participate is influenced by information and participation within their social network, and whether there are threshold or free-riding effects. The proposed experiments will randomize information dissemination methods and incentives for individuals and networks to participate in community activities across villages in India. Network and individual participation data will be collected through surveys to analyze the impact of social networks and information on collective action.
The document discusses different types of network experiments and interventions. It describes (1) assigning roommates randomly to manipulate networks and assess peer effects, (2) using natural experiments to manipulate exposure over existing networks, and (3) interventions that use networks to affect change. Specifically, it covers exogenous network experiments that randomly assign relationships, issues with experimental assignment, and four types of interventions: targeting individuals, segmentation, induction, and alteration.
01 Introduction to Networks Methods and Measuresdnac
This document provides an introduction to social network analysis. It discusses how networks matter through two fundamental mechanisms: connections and positions. Connections refer to the flow of things through networks, viewing networks as pipes. Positions refer to relational patterns and networks capturing role behavior, viewing networks as roles. The document also covers basic network data structures including nodes, edges, directed/undirected ties, binary/valued ties, and different levels of analysis such as ego networks and complete networks. It provides examples of one-mode and two-mode network data.
10 More than a Pretty Picture: Visual Thinking in Network Studiesdnac
Visualization has been important in network science since its beginnings to make invisible structures visible. While metrics can describe networks, visualizations allow researchers to see relationships and patterns across multiple dimensions that numbers alone cannot reveal. Effective network visualizations communicate insights that would be difficult to understand otherwise, by depicting global patterns and local details simultaneously in a way that builds intuition about the network's structure and generating processes. However, challenges include lack of consistent display frameworks, integrating too much multidimensional information, and issues of scale for large and dynamic networks.
09 Respondent Driven Sampling and Network Sampling with Memorydnac
RDS and network sampling methods aim to sample hidden populations for which traditional sampling frames do not exist. The document discusses issues with sampling hidden populations and evaluates Respondent Driven Sampling (RDS) and a new method called Network Sampling with Memory (NSM). It finds that RDS estimates can be biased when its assumptions are violated. A new data collection method called Inverse Preferential RDS (IP-RDS) and the NSM method show promise in improving estimation through modifications to the sampling process and collection of network data. Field testing is still needed to validate these innovative approaches.
06 Network Study Design: Ethical Considerations and Safeguardsdnac
This document outlines ethical considerations and safeguards for social network study design. It discusses principles from the Belmont Report including respect for persons, beneficence, and justice. Key risks in social network research are deductive disclosure, outing people, and legal or privacy risks from relational data. Mitigation strategies include data agreements, restricting access to identifying data, training researchers, and communicating clearly with IRBs. The document emphasizes that social network studies require safeguarding participant and alter privacy.
Random graphs and graph randomization procedures can be used for inference, simulation, and measuring networks. [1] Erdos random graphs are the simplest random graphs where each edge has an equal probability of being present. [2] More complex random graph models can be generated that preserve properties like degree distributions or mixing patterns observed in real networks. [3] Analyzing the distribution of triadic subgraphs (motifs) in a network compared to random graphs can test hypothesized mechanisms of network formation.
This document discusses considerations for collecting social network data through surveys. It addresses research design elements like defining the relevant population boundaries and sampling approaches. For surveys specifically, it covers informed consent, name generator questions to identify social ties, response formats, and balancing depth of network detail collected versus sample size. The key challenges are defining the theoretical population of interest, collecting a sufficiently large and representative network sample, and designing survey questions that accurately capture social ties within time and resource constraints.
Astute Business Solutions - Fast Track Impact Analysis for PeopleSoft 9.2 Upg...Arvind Rajan
Fast Track Impact Analyzer and Accelerated Upgrade for PeopleSoft provides tools and services to assess and accelerate PeopleSoft upgrades. It uses an automated assessment tool to quickly analyze upgrade efforts and produce reliable estimates. This helps clients upgrade more quickly and reduces risks and costs compared to traditional manual approaches. Services include upgrade planning, testing, and support.
The document discusses requirements engineering for a new customer data management system. It describes approaches for identifying business needs, defining internal requirements, and analyzing requirements against external client needs. The key steps outlined include eliciting requirements through workshops, interviews, questionnaires and documentation; analyzing and categorizing requirements; modeling system context and flows; and presenting requirements for validation and change management.
Software Requirements Specification (SRS) - Records Management with Chatbot A...FedericoLibadiaII
The purpose of this document is to build a computerized barangay record management
system to organize the files and to lessen the barangay officials' paper works. They could complete
things more quickly, and a large workforce wouldn't be required. The system's purpose is also to
offer safer storage, quick file retrieval, preserve dependability and correctness, faster file tracking,
and enhance communication quality. The staff finds that offering software to replace manual
record-keeping and the hand-issuing of barangay clearances, certificates, and other barangay
insurances to be of great assistance. This approach also suggests creating a chatbot assistant that
locals can use to rapidly find answers to their inquiries on the barangay website. This chatbot can
initiate polite dialogues, respond to barangay information, address frequently asked questions, and
provide details on the locations, people to contact, and activities of the barrio. Additionally, the
chatbot can aid in enhancing communication between the barangay and its residents. As the
barangay is frequently the subject of questioning from the locals.
A university management system is a software application designed to help universities manage their academic and administrative operations more efficiently. It provides a centralized platform for managing student records, course schedules, faculty information, financial transactions, and other important aspects of university operations.
With a university management system, administrators and faculty members can easily access and update student records, track course schedules and enrollment, and generate reports on academic performance and financial transactions. This system helps to streamline operations and reduce administrative burden, allowing faculty members to focus on teaching and research and students to focus on their studies.
1. The document describes the features of a student health assessment and management system.
2. The system is divided into 3 main sections: administration, data entry and reporting, and report and data analysis.
3. The administration section allows managing student, class, user and assessment cycle data, while the data entry section enables inputting student health assessments.
4. The report section generates various individual and consolidated reports on students' health statuses.
Introduction to Process Mining and its applicability to enterprise technology customer support. Please visit my blog at http://www.haimtoeg.com/?p=1881 for further discussion.
The Rational Unified Process (RUP) is a software engineering process that provides a disciplined approach to development. It aims to ensure high-quality software is produced within budget and on schedule. RUP supports object-oriented techniques and uses the Unified Modeling Language. It consists of inception, elaboration, construction, and transition phases with iterations. Benefits of RUP include better control over software, resolving risks, supporting changes and iterative development.
Crédits d’impôts CICE, CII, CIR, CIJV, CIE & Statut de JEI dans le domaine Mu...Marseille Innovation
Informations sur les dernières mesures fiscales JEI, CIR, CII dans le secteur des Médias
Marseille Innovation vous propose un petit déjeuner d’information sur les dernières mesures annoncées :
Crédit d'Impôt Innovation (CII), Crédit d’Impôt Recherche (CIR), statut de Jeune Entreprise Innovante (JEI),
Les spécificités pour les entreprises du secteur des Médias, contenus, jeux, et applications en ligne
Comment bénéficier de ce dispositif, quels sont les critères d’éligibilité
Informations sur le Crédit d’Impôt Jeu vidéo
En quoi consiste le crédit d'impôt jeux vidéo
Comment bénéficier de ce dispositif, quels sont les critères d’éligibilité
This document contains the resume of Satish Damodhar Pund. It outlines his professional experience as a software tester working on insurance and energy domain projects. It details his technical skills including Java, databases, testing tools and mainframe experience. It provides summaries of his roles and responsibilities on past projects for clients like IBM, Accenture and others. The resume highlights his testing abilities around requirements, test case design, defect logging and tracking, and ensuring quality standards.
A document discusses various software estimation techniques including function point analysis, COCOMO models, and cost drivers. Function point analysis breaks a system into functional components like inputs, outputs, inquiries and files that are assigned complexity weights and counts. COCOMO models like COCOMO I and COCOMO II estimate effort using size of the project and cost multipliers related to attributes of the product, computer system, personnel and project. Cost drivers help assess these multipliers to refine effort estimates.
This document provides an overview of exponential random graph models (ERGMs) for statistically modeling social networks. It discusses the goals of using ERGMs, which are to understand structural features of networks, test hypotheses about network formation processes, and link macro network structures to micro behaviors. Example model terms that can be used in ERGMs are described, ranging from simple models with just edges to more complex models incorporating triangles, degree distributions, and homophily. The document outlines the challenges of estimating ERGM parameters using maximum likelihood due to the normalizing constant, and notes that simulation-based approximations are typically used.
The document discusses network diffusion and peer influence. It covers compartmental models of diffusion, how network structure affects diffusion through factors like distance, clustering, and highly connected nodes. Simulation studies show networks with shorter path distances, more independent paths between nodes, and higher clustering coefficients diffuse ideas and behaviors more quickly. The regression analysis finds these network structural characteristics strongly predict a network's relative diffusion ratio compared to random networks.
This document provides an overview of ego network analysis. It defines ego networks as consisting of a focal individual (ego) and the people they are connected to (alters). Various measures of ego network composition, structure, and properties can be analyzed, such as size, density, and homophily. These measures provide insight into an individual's social support and influence, and can be used to study health-related questions by examining the characteristics and behaviors present in one's social network. Ego network data is relatively easy to collect and can offer information about both individuals and inferred properties of broader social networks.
12 Network Experiments and Interventions: Studying Information Diffusion and ...dnac
This document summarizes research on studying information diffusion and collective action through network experiments and interventions. The research aims to identify optimal strategies for information dissemination for public policy by comparing the effectiveness of different dissemination methods, including using phone/IVR, government representatives, and social network seeds. It also examines how an individual's decision to participate is influenced by information and participation within their social network, and whether there are threshold or free-riding effects. The proposed experiments will randomize information dissemination methods and incentives for individuals and networks to participate in community activities across villages in India. Network and individual participation data will be collected through surveys to analyze the impact of social networks and information on collective action.
The document discusses different types of network experiments and interventions. It describes (1) assigning roommates randomly to manipulate networks and assess peer effects, (2) using natural experiments to manipulate exposure over existing networks, and (3) interventions that use networks to affect change. Specifically, it covers exogenous network experiments that randomly assign relationships, issues with experimental assignment, and four types of interventions: targeting individuals, segmentation, induction, and alteration.
01 Introduction to Networks Methods and Measuresdnac
This document provides an introduction to social network analysis. It discusses how networks matter through two fundamental mechanisms: connections and positions. Connections refer to the flow of things through networks, viewing networks as pipes. Positions refer to relational patterns and networks capturing role behavior, viewing networks as roles. The document also covers basic network data structures including nodes, edges, directed/undirected ties, binary/valued ties, and different levels of analysis such as ego networks and complete networks. It provides examples of one-mode and two-mode network data.
10 More than a Pretty Picture: Visual Thinking in Network Studiesdnac
Visualization has been important in network science since its beginnings to make invisible structures visible. While metrics can describe networks, visualizations allow researchers to see relationships and patterns across multiple dimensions that numbers alone cannot reveal. Effective network visualizations communicate insights that would be difficult to understand otherwise, by depicting global patterns and local details simultaneously in a way that builds intuition about the network's structure and generating processes. However, challenges include lack of consistent display frameworks, integrating too much multidimensional information, and issues of scale for large and dynamic networks.
09 Respondent Driven Sampling and Network Sampling with Memorydnac
RDS and network sampling methods aim to sample hidden populations for which traditional sampling frames do not exist. The document discusses issues with sampling hidden populations and evaluates Respondent Driven Sampling (RDS) and a new method called Network Sampling with Memory (NSM). It finds that RDS estimates can be biased when its assumptions are violated. A new data collection method called Inverse Preferential RDS (IP-RDS) and the NSM method show promise in improving estimation through modifications to the sampling process and collection of network data. Field testing is still needed to validate these innovative approaches.
06 Network Study Design: Ethical Considerations and Safeguardsdnac
This document outlines ethical considerations and safeguards for social network study design. It discusses principles from the Belmont Report including respect for persons, beneficence, and justice. Key risks in social network research are deductive disclosure, outing people, and legal or privacy risks from relational data. Mitigation strategies include data agreements, restricting access to identifying data, training researchers, and communicating clearly with IRBs. The document emphasizes that social network studies require safeguarding participant and alter privacy.
Random graphs and graph randomization procedures can be used for inference, simulation, and measuring networks. [1] Erdos random graphs are the simplest random graphs where each edge has an equal probability of being present. [2] More complex random graph models can be generated that preserve properties like degree distributions or mixing patterns observed in real networks. [3] Analyzing the distribution of triadic subgraphs (motifs) in a network compared to random graphs can test hypothesized mechanisms of network formation.
This document discusses considerations for collecting social network data through surveys. It addresses research design elements like defining the relevant population boundaries and sampling approaches. For surveys specifically, it covers informed consent, name generator questions to identify social ties, response formats, and balancing depth of network detail collected versus sample size. The key challenges are defining the theoretical population of interest, collecting a sufficiently large and representative network sample, and designing survey questions that accurately capture social ties within time and resource constraints.
This document provides an overview of community detection in networks. It begins with an introduction to the concept of communities and their usefulness in network analysis. It then discusses two main approaches to calculating communities - descriptive methods like modularity, and generative methods like stochastic block models. The document notes that community detection is an active area of research, with opportunities to extend current methods. It provides several examples of community detection applications and acknowledges contributions from other researchers in the field.
This document provides an overview of stochastic actor-oriented models (SAOMs), including:
1. The general components of SAOMs including network and behavior objective functions that determine how and when actors change their ties and behaviors.
2. The estimation procedure which uses simulations to refine parameter estimates and minimize the deviation between simulated and observed network statistics.
3. Examples of how to interpret the output including checking for convergence of the model to the observed data.
How can we rely upon Social Network Measures? Agent-base modelling as the nex...Bruce Edmonds
All social network analysis of observed systems rely on assumptions, for example: how a link is defined is the right one, how the resulting network is analysed actually corresponds with our conclusions about it, etc. In other words the representation+analysis is a *model* of what we observe. Any model is fallible and thus needs independent validation, but this is rarely done in social network analysis due to the cost. Indeed, the only check is often that of face validity by the same person who collected the data and analysed it!
This lack of established validity is somewhat hidden by the divide within the field of social networks between the "formalists" who prove abstract properties of networks and those who apply its techniques to observed cases (who I will call "practioners"). The formalists might propose SN measures and prove their properties, but do not say anything about their applicability to any observed system. The practioners often proceed as if the measures will "work" on their networks - e.g. that a measure of centrality will tend to highlight the most influential actors.
However, agent-based models (ABM) might offer a potential solution to this. If a measure (or other SN technique) does not work with a plausible ABM of the phenomena (where we can actually check this), then we certainly can not rely on it for a similar model of observed phenomena. Some results and examples of this are given. Rather, it might be that SNA might be more reliable as a secondary analysis -- a model of a complex ABM of observed phenomena.
This document provides an overview of a workshop on structural equation modeling (SEM) presented by Dr. Siddhi Pittayachawan. The overview includes: an introduction to the speaker and their qualifications/research interests; a morning session covering causality, SEM vs regression, and path analysis; an afternoon session on measurement models, factor analysis, reliability analysis, and structural models; and a detailed timeline of topics to be covered.
Self-organization of society: fragmentation, disagreement, and how to overcom...Hiroki Sayama
This document summarizes a presentation on self-organization of society. It discusses how social fragmentation, disagreement and extremism can emerge from decentralized interactions between individuals seeking conformity and homophily. Three recent papers are summarized that show how social networks can become polarized through adaptive dynamics, how enhanced information gathering can intensify disagreement, and how behavioral diversity among individuals can allow for both cultural diversity and network connectivity in society. The key messages are that individual and collective outcomes may not align, and behavioral heterogeneity presents opportunities for diverse yet cohesive social outcomes.
Harnessing social signals to enhance a searchIsmail BADACHE
This paper describes an approach of information retrieval which takes into account social signals associated with Web resources to estimate its relevance to a query. We show how these data, which are in the form of actions within social activities (e.g. like, tweet), can be exploited to quantify social properties such as popularity and reputation. We propose a model that combines the social relevance, estimated from these properties, with the conventional textual relevance. We evaluated the effectiveness of our approach on IMDb dataset containing 32706 resources and their social characteristics collected from several social networks. We used also the selected criteria to learn models to determine their effectiveness in information retrieval. Our experimental results are promising and show the interest of integrating social signals in retrieval model to enhance a search.
Prote-OMIC Data Analysis and VisualizationDmitry Grapov
Introductory lecture to multivariate analysis of proteomic data.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Random Walk by User Trust and Temporal Issues toward Sparsity Problem in Soci...Sc Huang
Random Walk by User Trust and Temporal Issues toward Sparsity Problem in Social Tagging Recommender Systems
The document discusses using random walks and temporal factors to address sparsity problems in social tagging recommender systems. It introduces related work on item-based collaborative filtering, random walk recommendations, and models that learn influence probabilities. It then describes using random walks starting from users or items, and incorporating trust networks and influence powers to provide recommendations. Finally, it discusses addressing cold start problems, temporal decay issues, and experiment design.
Collective Spammer Detection in Evolving Multi-Relational Social NetworksTuri, Inc.
This document describes research on detecting collective spammer behavior in evolving multi-relational social networks. The researchers analyzed a sample of over 300 million users from Tagged.com, extracting graph structure and sequential features to classify users as spammers or legitimate with up to 88% accuracy. They also improved abuse reporting systems by incorporating reporter credibility and collective reasoning, achieving results with 87% AUC.
Billions of users interact intensively every day via Online Social Networks (OSNs) such as Facebook, Twitter, or Google+. This makes OSNs an invaluable source of information, and channel of actuation, for sectors like advertising, marketing, or politics. To get the most of OSNs, analysts need to identify influential users that can be leveraged for promoting products, distributing messages, or improving the image of companies. We propose a new unsupervised method, Massive Unsupervised Outlier Detection (MUOD), based on outliers detection, for providing support in the identification of influential users. MUOD is scalable, and can hence be used in large OSNs. Applying MUOD to a subset of roughly 400 million Google+ users, it has allowed identifying and discriminating automatically sets of outlier users, which present features associated to different definitions of influential users, like capacity to attract engagement, capacity to attract a large number of followers, or high infection capacity.
Recommender Systems: Advances in Collaborative FilteringChangsung Moon
This document summarizes recommender systems, focusing on collaborative filtering techniques. It discusses how recommender systems help with information overload by matching users with relevant items. Collaborative filtering is introduced as a technique that seeks to predict user preferences based on other similar users' ratings. The document then covers various collaborative filtering algorithms like neighborhood models, latent factor models using matrix factorization, and extensions like adding biases and temporal dynamics. It concludes by discussing hybrid methods and providing references for further reading.
This document discusses simulation as a research method in social science. It provides examples of different types of simulation models used in research:
- System dynamics models examine complex causality and feedback in systems over time.
- NK fitness landscape models study how modular systems adapt to fitness landscapes.
- Genetic algorithms model evolutionary adaptation of populations to optimal forms.
- Cellular automata emerge macro patterns from micro interactions of agents.
- Stochastic processes incorporate probabilistic distributions into systems.
The document outlines how these simulations can help answer research questions and provide insights when direct experiments are impossible or unethical.
This document provides an overview of a course on computational modelling for the social sciences. It introduces computational modelling as a methodology that uses models to study and solve complex problems in social phenomena. It discusses different types of models like conceptual, mathematical, physical and computational models. It explains key computational modelling approaches used in social sciences like social simulation, agent-based models, social network analysis and management information systems. The document outlines the course structure and provides contact and software details.
The document discusses brainstorming techniques for populating a cause and effect diagram to identify all possible causes of an issue. It provides examples of categories to organize causes, such as materials, measurement, people, and environment. Questions are suggested for each category to help identify root causes. Causes identified in the diagram should then be classified as controllable, procedural, or noise to determine which causes can be addressed.
Knowledge Identification using Rough Set Theory in Software Development Proce...ijcnes
The knowledge processing system leads the power of the organization in the world business race. All the industries are adopting knowledge management system for their human capital .The level of interaction occurs among the employees in the industry increase the knowledge creation, identification, representation and utilization. The knowledge discovery data process complexity various depend on the domain, nature of the applications, organizational system and many more organizational policies. The process time and volume of data is to be reduced for the decision supporting and Knowledge data discovery process using rough set theory equivalence association in the software development process and Information Technology Organization. Determination of the target factor variables that influence the processing knowledge in the organization .The variables are identified based equivalence association of all combinational factors of the variables. The researcher paper observed software development project, which produced un-deterministic result of the project development. This paper aimed to find the relations of variable, which could contribute more knowledge for the successful completion and delivery of the project that increase the software process development delivery. However, the activity variables leads to determine the set of activities carried out the professional group and encourage them to provide more attention on the selective activities.
"Reporting Complexity (with Complexity):General Systems Theory, Complexity and Simulation Modeling"
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Social Network Analysis based on MOOC's (Massive Open Online Classes)ShankarPrasaadRajama
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This document discusses policy informatics at a societal scale for massively interactive socially-coupled systems. It argues that policy modeling must be responsive to evidence-based policymaking and end simplistic prediction models. With embedded computing and social networks, individual reasoning is socially influenced. It proposes using synthetic data and simulations of interacting individuals and infrastructure to model these complex systems, while preserving privacy. This would involve generating synthetic populations and networks that represent regional interactions and integrate with built systems.
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Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
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3. When to use a SAOM
May 20, 2016 Duke Social Networks & Health Workshop 3
• Ques+ons about changes in network structure over +me
– Including mul+ple networks
– Including two-mode networks (selec+ng into foci)
• Ques+ons about how networks affect individual “behaviors,”
such as through peer influence
– Including mul+ple behaviors and possible reciprocal
effects
• Ques+ons about the endogenous associa+on between
networks and behavior
12. j3
ego
j4
j2
j1
Network Decision
€
fego(β,x) = -2
€
xij
j
∑ + 1.8
€
xij x ji
j
∑
outdegree reciprocity
€
fego(β,x) = -2
€
xij
j
∑ + 1.8
€
xij x ji
j
∑fego(β,x) = -2
€
xij
j
∑ + 1.8
€
xij x ji
j
∑
During a micro step, an actor evaluates how changing its outgoing
+e in each dyad would affect the value of the objec+ve func+on
(goal is to maximize the value of the func+on)
ego j1 j2 j3 j4
ego - 1 1 0 0
j1 1 - 0 0 0
j2 0 0 - 0 0
j3 1 0 0 - 0
j4 0 0 0 0 -
May 20, 2016 Duke Social Networks & Health Workshop 12
If… outdegree reciprocity sum
No change -2 * 2 = -4 1.8 * 1 = 1.8 -2.2
Drop j1 -2 * 1 = -2 1.8 * 0 = 0 -2
Drop j2 -2 * 1 = -2 1.8 * 1 = 1.8 -.2
Add j3 -2 * 3 = -6 1.8 * 3 = 3.6 -2.4
Add j4 -2 * 3 = -6 1.8 * 1 = 1.8 -4.2
Given the current state of the network, ego is
most likely to drop the ?e to j2, because that
decision maximizes the objec+ve func+on
13. • Outdegree always present
• Network processes (e.g., reciprocity, transi+vity)
• Adribute based:
– Sociality: effect of adribute on outgoing +es
– Popularity: effect of behavior on incoming +es
– Homophily: ego-alter similarity
– Note: adributes may be stable or +me-changing
(exogenous or endogenously modeled)
• Dyadic adributes (e.g., co-membership)
May 20, 2016 Duke Social Networks & Health Workshop 13
Network Objec?ve Func?on Effects
21. Behavior Decision*
May 20, 2016 Duke Social Networks & Health Workshop 21
If… linear quad age similarity sum
Drop to 0 -.5 * 0 = 0 .25 * 0 = 0 .1 * 10 * 0 = 0 1 * .35 = .35 .35
Stay at 1 -.5 * 1 = -.5 .25 * 1 = .25 .1 * 10 * 1 = 1 1 * .95 = .95 1.7
Up to 2 -.5 * 2 = -1 .25 * 4 = 1 .1 * 10 * 2 = 2 1 * -.55 = -.55 1.45
* Assume covariates uncentered
Second, calculate the contribu+ons for
each of the other effects
22. Behavior Decision*
May 20, 2016 Duke Social Networks & Health Workshop 22
If… linear quad age similarity sum
Drop to 0 -.5 * 0 = 0 .25 * 0 = 0 .1 * 10 * 0 = 0 1 * .35 = .35 .35
Stay at 1 -.5 * 1 = -.5 .25 * 1 = .25 .1 * 10 * 1 = 1 1 * .95 = .95 1.7
Up to 2 -.5 * 2 = -1 .25 * 4 = 1 .1 * 10 * 2 = 2 1 * -.55 = -.55 1.45
* Assume covariates uncentered
These effects pull
ego toward the
extremes
The posi+ve age b
pushes ego’s
behavior upward
Similarity pushes
ego to stay the
same
Altogether, the greatest contribu+on to the behavior func+on comes
from ego choosing to maintain the same behavior level
23. • Necessary for both network and behavior
• Determine the wai+ng +me un+l actor’s chance to make decisions
• Func+on of observed changes
– But not the same as the number of changes observed
– Separate rate parameter for each period between observa+ons
• Wai+ng +me distributed uniformly by default, but differences can
be modeled based on:
• Actor adributes: do some types of actors experience more or
less change
• Degree: do actors with more/fewer +es experience a different
volume of change
May 20, 2016 Duke Social Networks & Health Workshop 23
Rate Func?ons
32. • Helpful to imagine the network func+on as a logis+c regression
– Unit of analysis: dyad
– Outcome: +e presence (keeping or adding) vs. absence
(dissolving or failing to add)
– Each effect represents how a one-unit change in the effect
sta+s+c affects the log-odds of a +e, all else being equal
• Some effects interpretable using odds ra+os, but
– One-unit changes may not be meaningful
– All else is never equal (any change also affects the
outdegree count, at a minimum)
• Behavior func+on specifies how a one-unit change in the effect
sta+s+c affects the odds of increasing behavior one unit
May 20, 2016 Duke Social Networks & Health Workshop 32
Interpre?ng Results
33. Network func?on b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transi+ve triplets .52 *** .04
Popularity .29 *** .04
Extracurric. act. overlap .28 *** .06
Smoke similarity .68 *** .12
Smoke alter .14 ** .05
Smoke ego -.04 .05
Female similarity .24 *** .04
Female alter -.11 * .05
Female ego -.04 .05
Age similarity 1.00 *** .13
Age alter -.01 .03
Age ego -.04 .03
Delinquency similarity .15 .08
Delinquency alter -.04 .04
Delinquency ego .02 .04
Alcohol similarity .27 ** .10
Alcohol alter -.03 .03
Alcohol ego -.03 .04
GPA similarity .70 *** .13
GPA alter -.05 .04
GPA ego -.02 .04
From Schaefer, D.R. S.A. Haas, and N. Bishop. 2012. “A Dynamic
Model of US Adolescents’ Smoking and Friendship Networks.”
American Journal of Public Health, 102:e12-e18.
May 20, 2016 Duke Social Networks & Health Workshop 33
Rate: Each actor is given ~10
micro steps in which to make a
change to its network
• Add a +e, drop a +e, or make
no change
Rate
34. Network func?on b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transi+ve triplets .52 *** .04
Popularity .29 *** .04
Extracurric. act. overlap .28 *** .06
Smoke similarity .68 *** .12
Smoke alter .14 ** .05
Smoke ego -.04 .05
Female similarity .24 *** .04
Female alter -.11 * .05
Female ego -.04 .05
Age similarity 1.00 *** .13
Age alter -.01 .03
Age ego -.04 .03
Delinquency similarity .15 .08
Delinquency alter -.04 .04
Delinquency ego .02 .04
Alcohol similarity .27 ** .10
Alcohol alter -.03 .03
Alcohol ego -.03 .04
GPA similarity .70 *** .13
GPA alter -.05 .04
GPA ego -.02 .04
From Schaefer, D.R. S.A. Haas, and N. Bishop. 2012. “A Dynamic
Model of US Adolescents’ Smoking and Friendship Networks.”
American Journal of Public Health, 102:e12-e18.
May 20, 2016 Duke Social Networks & Health Workshop 34
Outdegree: The nega+ve sign is
typical. It means that +es are
unlikely, unless other effects in
the model make a posi+ve
contribu+on to the network
func+on.
density
35. Network func?on b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transi+ve triplets .52 *** .04
Popularity .29 *** .04
Extracurric. act. overlap .28 *** .06
Smoke similarity .68 *** .12
Smoke alter .14 ** .05
Smoke ego -.04 .05
Female similarity .24 *** .04
Female alter -.11 * .05
Female ego -.04 .05
Age similarity 1.00 *** .13
Age alter -.01 .03
Age ego -.04 .03
Delinquency similarity .15 .08
Delinquency alter -.04 .04
Delinquency ego .02 .04
Alcohol similarity .27 ** .10
Alcohol alter -.03 .03
Alcohol ego -.03 .04
GPA similarity .70 *** .13
GPA alter -.05 .04
GPA ego -.02 .04
From Schaefer, D.R. S.A. Haas, and N. Bishop. 2012. “A Dynamic
Model of US Adolescents’ Smoking and Friendship Networks.”
American Journal of Public Health, 102:e12-e18.
May 20, 2016 Duke Social Networks & Health Workshop 35
Reciprocity: Ties that create a
reciprocated +e are more likely
to be added or maintained. This
effect hovers around 2 in
friendship-type network.
recip
36. Network func?on b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transi+ve triplets .52 *** .04
Popularity .29 *** .04
Extracurric. act. overlap .28 *** .06
Smoke similarity .68 *** .12
Smoke alter .14 ** .05
Smoke ego -.04 .05
Female similarity .24 *** .04
Female alter -.11 * .05
Female ego -.04 .05
Age similarity 1.00 *** .13
Age alter -.01 .03
Age ego -.04 .03
Delinquency similarity .15 .08
Delinquency alter -.04 .04
Delinquency ego .02 .04
Alcohol similarity .27 ** .10
Alcohol alter -.03 .03
Alcohol ego -.03 .04
GPA similarity .70 *** .13
GPA alter -.05 .04
GPA ego -.02 .04
From Schaefer, D.R. S.A. Haas, and N. Bishop. 2012. “A Dynamic
Model of US Adolescents’ Smoking and Friendship Networks.”
American Journal of Public Health, 102:e12-e18.
May 20, 2016 Duke Social Networks & Health Workshop 36
Transi?ve triplets: Ties that
create more transi+ve triads
have a greater likelihood.
• Should also test interac+on
with Reciprocity (usually
nega+ve)
transTrip
37. Network func?on b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transi+ve triplets .52 *** .04
Popularity .29 *** .04
Extracurric. act. overlap .28 *** .06
Smoke similarity .68 *** .12
Smoke alter .14 ** .05
Smoke ego -.04 .05
Female similarity .24 *** .04
Female alter -.11 * .05
Female ego -.04 .05
Age similarity 1.00 *** .13
Age alter -.01 .03
Age ego -.04 .03
Delinquency similarity .15 .08
Delinquency alter -.04 .04
Delinquency ego .02 .04
Alcohol similarity .27 ** .10
Alcohol alter -.03 .03
Alcohol ego -.03 .04
GPA similarity .70 *** .13
GPA alter -.05 .04
GPA ego -.02 .04
From Schaefer, D.R. S.A. Haas, and N. Bishop. 2012. “A Dynamic
Model of US Adolescents’ Smoking and Friendship Networks.”
American Journal of Public Health, 102:e12-e18.
May 20, 2016 Duke Social Networks & Health Workshop 37
Indegree Popularity: Actors with
more incoming +es have a
greater likelihood of receiving
future +es
inPop
38. Network func?on b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transi+ve triplets .52 *** .04
Popularity .29 *** .04
Extracurric. act. overlap .28 *** .06
Smoke similarity .68 *** .12
Smoke alter .14 ** .05
Smoke ego -.04 .05
Female similarity .24 *** .04
Female alter -.11 * .05
Female ego -.04 .05
Age similarity 1.00 *** .13
Age alter -.01 .03
Age ego -.04 .03
Delinquency similarity .15 .08
Delinquency alter -.04 .04
Delinquency ego .02 .04
Alcohol similarity .27 ** .10
Alcohol alter -.03 .03
Alcohol ego -.03 .04
GPA similarity .70 *** .13
GPA alter -.05 .04
GPA ego -.02 .04
From Schaefer, D.R. S.A. Haas, and N. Bishop. 2012. “A Dynamic
Model of US Adolescents’ Smoking and Friendship Networks.”
American Journal of Public Health, 102:e12-e18.
May 20, 2016 Duke Social Networks & Health Workshop 38
Dyadic Covariate: Actors who
share an extracurricular ac+vity
(coded 1) are more likely to have
a friendship +e
X
39. Network func?on b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transi+ve triplets .52 *** .04
Popularity .29 *** .04
Extracurric. act. overlap .28 *** .06
Smoke similarity .68 *** .12
Smoke alter .14 ** .05
Smoke ego -.04 .05
Female similarity .24 *** .04
Female alter -.11 * .05
Female ego -.04 .05
Age similarity 1.00 *** .13
Age alter -.01 .03
Age ego -.04 .03
Delinquency similarity .15 .08
Delinquency alter -.04 .04
Delinquency ego .02 .04
Alcohol similarity .27 ** .10
Alcohol alter -.03 .03
Alcohol ego -.03 .04
GPA similarity .70 *** .13
GPA alter -.05 .04
GPA ego -.02 .04
Ties driven by similarity on:
Gender (could use “same” effect)
Age
Alcohol use
GPA
Females less adrac+ve as friends
than males.
From Schaefer, D.R. S.A. Haas, and N. Bishop. 2012. “A Dynamic
Model of US Adolescents’ Smoking and Friendship Networks.”
American Journal of Public Health, 102:e12-e18.
altX
egoX
simX
May 20, 2016 Duke Social Networks & Health Workshop 39
40. Network func?on b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transi+ve triplets .52 *** .04
Popularity .29 *** .04
Extracurric. act. overlap .28 *** .06
Smoke similarity .68 *** .12
Smoke alter .14 ** .05
Smoke ego -.04 .05
Female similarity .24 *** .04
Female alter -.11 * .05
Female ego -.04 .05
Age similarity 1.00 *** .13
Age alter -.01 .03
Age ego -.04 .03
Delinquency similarity .15 .08
Delinquency alter -.04 .04
Delinquency ego .02 .04
Alcohol similarity .27 ** .10
Alcohol alter -.03 .03
Alcohol ego -.03 .04
GPA similarity .70 *** .13
GPA alter -.05 .04
GPA ego -.02 .04
From Schaefer, D.R. S.A. Haas, and N. Bishop. 2012. “A Dynamic
Model of US Adolescents’ Smoking and Friendship Networks.”
American Journal of Public Health, 102:e12-e18.
Ties driven by similarity on
smoking behavior.
Smokers more adrac+ve as
friends than non-smokers.
Alter
Nonsmoker Smoker
Ego
Nonsmoker .25 -.19
Smoker -.51 .41
Similarity is an “interac+on” between
ego and alter, thus interpreta+on
requires considering the main effects
Ego-alter selec+on: Contribu+ons to
network objec+ve func+on by dyad type
May 20, 2016 Duke Social Networks & Health Workshop 40
47. Cumula?ve Indegree Distribu?on
Goodness of Fit of IndegreeDistribution
p: 0
Statistic
0 1 2 3 4 5 6 7 8
139
193
282
343
401
437
459
483
491
May 20, 2016 Duke Social Networks & Health Workshop 47
48. Geodesic Distribu?on
Goodness of Fit of GeodesicDistribution
p: 0.001
Statistic
1 2 3 4 5 6 7
1381
2795
5014
7772
10598
12081 11892
May 20, 2016 Duke Social Networks & Health Workshop 48
49. Triad Census Goodness of Fit of TriadCensus
p: 0.114
Statistic(centeredandscaled)
003 012 102 021D 021U 021C 111D 111U 030T 030C 201 120D 120U 120C 210 300
21286492
428358
129429
693
1141
1052
923
625
108
4 171
114
58
39
91
36
May 20, 2016 Duke Social Networks & Health Workshop 49
50. Smoking Distribu?on
Goodness of Fit of BehaviorDistribution
p: 1
Statistic
0 1 2
222
98
182
May 20, 2016 Duke Social Networks & Health Workshop 50
52. Extensions to Basic Model
May 20, 2016 Duke Social Networks & Health Workshop 52
• interac+ons
• event history outcomes
• mul+ple behaviors
• mul+ple network op+ons
• valued +es
• mul+level networks
• two mode networks
• increase vs. decrease in +es and/or behavior
• +me heterogeneity
• simula+ons (test interven+ons)
• ML, Bayes es+ma+on
53. Asymmetric Peer Influence
• Implicit assump+on that effects work the same for:
– Tie forma+on vs. dissolu+on
– Behavior increase vs. decrease
• Unrealis+c for smoking
– Physical/psychological dependence, social learning
• Easy to relax this assump+on
– Separate behavior objec+ve func+on into:
• Crea?on func?on: only considers increases
• Maintenance func?on: only considers decreases
– Could make similar dis+nc+on in the network func+on
May 20, 2016 Duke Social Networks & Health Workshop 53
56. Decomposing Network Homogeneity
Source Selec?on (%) Influence (%) Sample
Schaefer et al. 2012 40 34 U.S.
Mercken et al. 2009 17-47 6-23 Europe (6 countries)
Mercken et al. 2010 31-46 15-22 Finland
Steglich et al. 2010 25-34 20-37 Scotland
• How much network homogeneity on smoking is due to
selec?on vs. influence?
– Systema+cally set selec+on and influence parameters to
zero and simulate network-behavior co-evolu+on (see
Steglich et al. 2010)
May 20, 2016 Duke Social Networks & Health Workshop 56
59. Context Effects
How do these effects depend upon context?
• Randomly manipulate ini+al smoking prevalence
– 25% ini+al smokers up to 75%
• Randomly distribute smokers and nonsmokers across the
network
– Similar results with empirical and model-based
manipula+ons
• Full results in adams, jimi & David R. Schaefer. 2016. “How
Ini+al Prevalence Moderates Network-Based Smoking
Change: Es+ma+ng Contextual Effects with Stochas+c Actor
Based Models.” Journal of Health & Social Behavior 57(1):
22-38.
May 20, 2016 Duke Social Networks & Health Workshop 59
62. • Ties are more or less enduring states
– Plausible for friendship or collabora+ons
– Not useful for “event” data (e.g. phone calls)
• Change occurs in con+nuous +me
• Markov process: future state only a func+on of current state
– No lagged effects, “grudges”
• Actors control outgoing +es and behavior
• One change at a +me
– No coordinated or simultaneous changes
May 20, 2016 Duke Social Networks & Health Workshop 62
Assump?ons
63. • Up to 10% probably ok, more than 20% likely a problem
• Endogenous network & behavior imputa+on
– Missing values at t0 set to 0 (network) or mode (behavior)
– Missing values at t1+ imputed with last valid value if
possible, otherwise 0
• Covariates imputed with the mean
– Other values can be specified
• Imputed values are treated as non-informa+ve, thus not used
in calcula+ng target sta+s+cs
– Convergence and fit are determined based only upon
observed cases
May 20, 2016 Duke Social Networks & Health Workshop 63
Missing Data
64. Good Sources of Informa?on
May 20, 2016 Duke Social Networks & Health Workshop 64
• RSiena manual
• Snijders, van de Bunt & Steglich, 2010
• Steglich, Snijders & Pearson, 2010
• Tom Snijders’ SIENA website
www.stats.ox.ac.uk/siena/
– Workshops
– Scripts
– Applica+ons in the literature
– Latest version of RSiena
– Link to stocnet listserv – important updates announced here
– “Siena_algorithms.pdf”
67. • One mode or two mode network with at least two
observa+ons, each represented as a matrix
– Ties coded 0, 1, 10 (structural 0), 11 (structural 1), or NA
• For each “period” between adjacent waves, stability measured
by the Jaccard coefficient should be at least .25
– Ties persisted / (+es formed + +es dissolved + +es persisted)
• “Complete network data” all actors w/in bounded semng
– Some turnover in set of actors allowed but same actors in
the data for each wave (even if not observed during wave)
– See manual for how to deal with composi+on change
• Recommended N: 30-2000
May 20, 2016 Duke Social Networks & Health Workshop 67
Data Structure: Network
68. • Dependent behaviors
– Time-varying adributes used as dependent variable(s)
– Coded as integer (e.g., 1-10)
– Last +me point is used
• Changing actor covariates
– Time-varying adributes used as independent variables
– Last +me point not used (only applicable for 3+ waves)
• Constant covariates
– Ex: age, sex, race/ethnicity, behavior
• Dyadic covariates
– Ex: semngs that drive contact
NOTE: Covariates are centered by default
May 20, 2016 Duke Social Networks & Health Workshop 68
Addi?onal Data Structures