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.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Slightly revised slide deck from previous upload. Presented at the 2018 annual convention of the Society of Gynecologic Oncologists of the Philippines,19 July 2018.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Presentation at the Netflix Expo session at RecSys 2020 virtual conference on 2020-09-24. It provides an overview of recommendation and personalization at Netflix and then highlights some of the things we’ve been working on as well as some important open research questions in the field of recommendations.
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
Social Media Mining - Chapter 6 (Community Analysis)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Slightly revised slide deck from previous upload. Presented at the 2018 annual convention of the Society of Gynecologic Oncologists of the Philippines,19 July 2018.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Presentation at the Netflix Expo session at RecSys 2020 virtual conference on 2020-09-24. It provides an overview of recommendation and personalization at Netflix and then highlights some of the things we’ve been working on as well as some important open research questions in the field of recommendations.
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
Social Media Mining - Chapter 6 (Community Analysis)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
Social Learning in Networks: Extraction Deterministic RulesDmitrii Ignatov
In this talk, we want to introduce experimental
economics to the field of data mining and vice versa. It continues
related work on mining deterministic behavior rules of human
subjects in data gathered from experiments. Game-theoretic
predictions partially fail to work with this data. Equilibria also
known as game-theoretic predictions solely succeed with experienced
subjects in specific games – conditions, which are rarely
given. Contemporary experimental economics offers a number of
alternative models apart from game theory. In relevant literature,
these models are always biased by philosophical plausibility
considerations and are claimed to fit the data. An agnostic
data mining approach to the problem is introduced in this
paper – the philosophical plausibility considerations follow after
the correlations are found. No other biases are regarded apart
from determinism. The dataset of the paper “Social Learning in
Networks” by Choi et al 2012 is taken for evaluation. As a result,
we come up with new findings. As future work, the design of a
new infrastructure is discussed.
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.
Online Social Networks have become a prominent mode of communication and collaboration. Link Prediction is a major issue in Social Networks. Though ample methods are proposed to solve it, most of them take a static view of the network. Social Networks are dynamic in nature, this aspect has to be accounted. In this paper we propose a novel predictor LCF for Link Prediction in dynamic networks. In this method we view Social Networks as sequence of snapshots, each snapshot is the state of the network of a particular time period. Each edge of the network is assigned a weight based on its time stamp. We compute the LCF score for all node pairs in the network to predict the associations that may occur at a future time in the Social Network. We have also shown that our predictor outperforms the standard baseline methods for Link Prediction
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.
Environmental impact assessment methodology by Dr. I.M. Mishra Professor, Dep...Arvind Kumar
Environmental impact assessment methodology by Dr. I.M. Mishra Professor, Dept. of Chemical Engineering Dean, Saharanpur Campus Indian Institute of Technology, Roorkee
Mining Frequent Patterns and Associations from the Smart meters using Bayesia...Eswar Publications
In today’s world migration of people from rural areas to urban areas is quite common. Health care services are one of the most challenging aspect that is must require to the people with abnormal health. Advancements in the technologies lead to build the smart homes, which contains various sensor or smart meter devices to automate the process of other electronic device. Additionally these smart meters can be able to capture the daily activities of the patients and also monitor the health conditions of the patients by mining the frequent patterns and
association rules generated from the smart meters. In this work we proposed a model that is able to monitor the activities of the patients in home and can send the daily activities to the corresponding doctor. We can extract the frequent patterns and association rules from the log data and can predict the health conditions of the patients and can give the suggestions according to the prediction. Our work is divided in to three stages. Firstly, we used to record the daily activities of the patient using a specific time period at three regular intervals. Secondly we applied the frequent pattern growth for extracting the association rules from the log file. Finally, we applied k means clustering for the input and applied Bayesian network model to predict the health behavior of the patient and precautions will be given accordingly.
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Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
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This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
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Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
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Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
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Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
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Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
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Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
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Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
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Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
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Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
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22 An Introduction to Stochastic Actor-Oriented Models (SAOM or Siena)
1. An Introduction to
Stochastic Actor-Oriented Models
(SAOM or SIENA)
Dr. David R. Schaefer
University of California, Irvine
Duke Social Networks & Health Workshop, 2019
2. 30-day smoking
None (0)
1-11 days (1)
12+ days (2)
Jefferson High (Add Health)
May 17, 2019 Duke Social Networks & Health Workshop 2
Smokers popular:
Smoking-indegree
correlation = .09*
Smoking homophily:
Odds ratio = 1.57***
3. Network Homogeneity on Smoking
Peer
Influence
or
Friend
Selection
time t
time t-1
A
C D
B
A
C D
B
A
C D
B
May 17, 2019 Duke Social Networks & Health Workshop 3
5. Inferring Network → Behavior
Requires controlling for network selection based on the behavior
(e.g., popularity, homophilous selection)
• In modeling the network, also good to account for
1. Similarity on attributes correlated with the behavior
2. Network processes (e.g., triad closure) that drive network
change & can amplify network-behavior patterns (see below)
May 17, 2019 Duke Social Networks & Health Workshop 5
C D
BA
I
Homophily
C D
BA
C D
BA
Homophily
through
Reciprocity
Homophily
through
Transitivity
6. When to use a SAOM
May 17, 2019 Duke Social Networks & Health Workshop 6
• Questions about how networks affect individual “behaviors”
– Peer influence
– Diffusion
• Questions about changes in network structure over time
– Social exclusion and withdrawal
– How do we acquire the networks who influence us?
• Questions about endogenous associations between networks
and behavior (and subsequent impacts)
– Cumulative disadvantage process
7. Outcome
Figure adapted from jimi adams
Modeled Interdependencies between Individuals
None w/in Dyad Dyad+
Individual
General
Linear
Model
Actor-Partner
Interdependence
(APIM)
Network
Autoregression
Stochastic
Actor-
Oriented
Model
(SAOM)Network Erdös-Renyi
(MR)QAP, Dyad
Independent
Model
ERGM,
Relational Event
Model
May 17, 2019 Duke Social Networks & Health Workshop 7
How do SAOMs relate to other models?
8. ERGM and SIENA Distinction
• Dynamic ERGM (e.g., TERGM, STERGM)
– Models Xt as a function of Xt-1
– Discrete change in network
• SIENA
– Conditional on Xt-1, assume sequence of unobserved micro-
steps to Xt
– A micro-step allows one actor to change one tie
– Model estimates the “rules” actors use to determine which
tie to change
– Ultimately, actors following these rules produce a network
resembling Xt
• For more details see: https://osf.io/preprints/socarxiv/6rm9q
May 17, 2019 Duke Social Networks & Health Workshop 8
9. Overview
1. The general form of the model
– Network function for relationship change
– Behavior function for “behavior” change
– Rate functions
2. Model estimation procedure
– Model assumptions
– MCMC estimation algorithm
3. Empirical example
4. Extensions & Miscellany
May 17, 2019 Duke Social Networks & Health Workshop 9
10. • Switch to “SIENA intuition.R”
May 17, 2019 Duke Social Networks & Health Workshop 10
11. May 17, 2019 Duke Social Networks & Health Workshop 11
See the visualization
How do we get
from this
to this?
12. 1. General SAOM Form
May 17, 2019 Duke Social Networks & Health Workshop 12
13. Stochastic Actor-Oriented Model
• Also called Stochastic Actor-Based Model (SABM), or “SIENA”
based on the software used to estimate the model
– Simulation Investigation for Empirical Network Analysis
– Currently estimable in R (RSiena)
• Recognition that networks and behavior are interdependent
– Behaviors can affect network structure
– Network structure can affect behavior
– Thus, both “outcomes” are endogenous
May 17, 2019 Duke Social Networks & Health Workshop 13
15. • Discrete change is modeled as occurring in continuous time
(between observations) through a sequence of micro steps
– Model takes the form of an AGENT-BASED MODEL
– Actors control their outgoing ties and behavior
– Functions specify when and how they change
SAOM Components
Decision Timing
(when changes occur)
Decision Rules
(how changes occur)
Network Evolution
Network rate
function
Network objective
function
Behavior Evolution
Behavior rate
function
Behavior objective
function
May 17, 2019 Duke Social Networks & Health Workshop 15
16. Network Objective Function
• Network change is modeled by allowing actors to select ties (by adding or
dropping them) based upon:
fi(β,x) is the value of the network objective function for actor i, given:
• the current set of parameter estimates (β)
• state of the network (x)
• For k effects, represented as ski, which may be based on
– the network (x), or individual attributes (z)
• Estimated with random disturbance (ε) associated with x, z, t and j
• Goal of model fitting is to estimate each βk
May 17, 2019 Duke Social Networks & Health Workshop 16
fi (b, x) = bkski
k
å (x)+e(x,z,t, j)
17. j3
ego
j4
j2
j1
Network Decision
fego(b,x) = -2 xij
j
å + 1.8 xij x ji
j
å
outdegree reciprocity
fego(b,x) = -2 xij
j
å + 1.8 xij x ji
j
åfego(b,x) = -2 xij
j
å + 1.8 xij x ji
j
å
During a micro step, an actor evaluates how changing its outgoing
tie in each dyad would affect the value of the objective function
(goal is to maximize the value of the function)
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 17, 2019 Duke Social Networks & Health Workshop 17
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 * 2 = 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 tie to j2, because that
decision maximizes the objective function
18. • Outdegree always in the model
• Network processes (e.g., reciprocity, transitivity)
• Attribute based:
– Sociality: effect of attribute on outgoing ties
– Popularity: effect of behavior on incoming ties
– Homophily: ego-alter similarity
– Note: attributes may be stable or time-changing
(exogenous or endogenously modeled)
• Dyadic attributes (e.g., co-membership)
May 17, 2019 Duke Social Networks & Health Workshop 18
Network Objective Function Effects
19. • Predict change in “behavior,” which is the generic term for an
individual attribute
– Refers to any attitude, belief, health factor, etc.
• Optional: SAOMs don’t require a behavior function, and they
may not be relevant for many questions
• Developed for ordinal measures of DVs (~2-10 levels)
• Goal is to estimate effect of network on behavior change
May 17, 2019 Duke Social Networks & Health Workshop 19
Behavior Objective Function
20. Behavior Objective Function
• Choice probabilities take the form of a multinomial logit
model instantiated by the objective function
where z represents the behavior
• The function dictates which level of the behavior actors adopt
– Actors evaluate all possible changes
• Increase/decrease by one unit, or no change
– Option with highest evaluation most likely
May 17, 2019 Duke Social Networks & Health Workshop 20
fi
z
(b, x,z) = bk
z
ski
z
k
å (x,z)+e(x,z,t,d)
Figure adapted from C. Steglich
21. • Linear term to control for distribution (quadratic term if the
behavior has 3+ levels)
• Predictors of peer influence
– Alters’ value on the behavior, or another attribute or
behavior
• Multiple specifications, including mean, minimum, maximum…
• Ego’s other behaviors or attributes (e.g., gender, age)
– Ego’s network position (e.g., degree)
– Interactions with reciprocity
May 17, 2019 Duke Social Networks & Health Workshop 21
Behavior Objective Function Effects
22. Behavior Decision
May 17, 2019 Duke Social Networks & Health Workshop 22
Linear effect
Quadratic effect
Attribute effect
(e.g. age)
Similarity effect
How attractive is each level of the behavior based on these
effects and (hypothetical) parameter estimates?
23. Behavior Decision*
May 17, 2019 Duke Social Networks & Health Workshop 23
If… linear quad age similarity sum
Drop to 0 -.5 * 0 = 0 .25 * 0 = 0 .1 * 10 * 0 = 0
Stay at 1 -.5 * 1 = -.5 .25 * 1 = .25 .1 * 10 * 1 = 1
Up to 2 -.5 * 2 = -1 .25 * 4 = 1 .1 * 10 * 2 = 2
The contributions for these effects are simple to calculate
* For simplicity, calculations treat covariates as uncentered. However, SIENA will center dependent behaviors,
thus for actual computations replace raw values in calculations with centered values.
24. Ego, j1 1 - | 1 - 1 | / 2 = 1 1 (1 - .05) = .95
Ego, j2 1 - | 1 - 1 | / 2 = 1 1 (1 - .05) = .95
Ego, j3 1 - | 1 - 0 | / 2 = .5 0 (.5 - .05) = 0
Ego, j4 1 - | 1 - 2 | / 2 = .5 0 (.5 - .05) = 0
Similarity statistic = 1.90
Behavior Decision*
May 17, 2019 Duke Social Networks & Health Workshop 24
J3(0)
Ego (1)
J4(2)
J1(1)
xij (simij
Z
- simZ
)
j
å where=
simZ
= similarity expected by chance= similarity expected by chance = .05
simij
Z
xij (simij
Z
-simZ
)
j2(1)
Maintain z=1
Calculate total similarity for each
of ego’s possible decisions
* For simplicity, calculations treat covariates as uncentered. However, SIENA will center dependent behaviors,
thus for actual computations replace raw values in calculations with centered values.
25. Ego, j1 1 - | 0 - 1 | / 2 = .5 1 (.5 - .05) = .45
Ego, j2 1 - | 0 - 1 | / 2 = .5 1 (.5 - .05) = .45
Ego, j3 1 - | 0 - 0 | / 2 = 1 0 (1 - .05) = 0
Ego, j4 1 - | 0 - 2 | / 2 = 0 0 (0 - .05) = 0
Similarity statistic = .90
Behavior Decision*
May 17, 2019 Duke Social Networks & Health Workshop 25
J3(0)
Ego (1)
J4(2)
J1(1)
Calculate total similarity for each
of ego’s possible decisions
xij (simij
Z
- simZ
)
j
å=
simZ
= similarity expected by chance= similarity expected by chance = .05
simij
Z
xij (simij
Z
-simZ
)
j2(1)
Decrease to z=0
where
* For simplicity, calculations treat covariates as uncentered. However, SIENA will center dependent behaviors,
thus for actual computations replace raw values in calculations with centered values.
26. Ego, j1 1 - | 2 - 1 | / 2 = .5 1 (.5 - .05) = .45
Ego, j2 1 - | 2 - 1 | / 2 = .5 1 (.5 - .05) = .45
Ego, j3 1 - | 2 - 0 | / 2 = 0 0 (0 - .05) = 0
Ego, j4 1 - | 2 - 2 | / 2 = 1 0 (1 - .05) = 0
Similarity statistic = .90
Behavior Decision*
May 17, 2019 Duke Social Networks & Health Workshop 26
J3(0)
Ego (1)
J4(2)
J1(1)
xij (simij
Z
- simZ
)
j
å=
simZ
= similarity expected by chance= similarity expected by chance = .05
simij
Z
xij (simij
Z
-simZ
)
j2(1)
Increase to z=2
Calculate total similarity for each
of ego’s possible decisions
where
* For simplicity, calculations treat covariates as uncentered. However, SIENA will center dependent behaviors,
thus for actual computations replace raw values in calculations with centered values.
27. Behavior Decision*
May 17, 2019 Duke Social Networks & Health Workshop 27
If… linear quad age similarity sum
Drop to 0 -.5 * 0 = 0 .25 * 0 = 0 .1 * 10 * 0 = 0 1 * .90 = .90 .90
Stay at 1 -.5 * 1 = -.5 .25 * 1 = .25 .1 * 10 * 1 = 1 1 * 1.90 = 1.90 2.65
Up to 2 -.5 * 2 = -1 .25 * 4 = 1 .1 * 10 * 2 = 2 1 * .90 = .90 3.90
Finally, calculate the contribution of each possible decision
* For simplicity, calculations treat covariates as uncentered. However, SIENA will center dependent behaviors,
thus for actual computations replace raw values in calculations with centered values.
28. Behavior Decision*
May 17, 2019 Duke Social Networks & Health Workshop 28
If… linear quad age similarity sum
Drop to 0 -.5 * 0 = 0 .25 * 0 = 0 .1 * 10 * 0 = 0 1 * .90 = .90 .90
Stay at 1 -.5 * 1 = -.5 .25 * 1 = .25 .1 * 10 * 1 = 1 1 * 1.90 = 1.90 2.65
Up to 2 -.5 * 2 = -1 .25 * 4 = 1 .1 * 10 * 2 = 2 1 * .90 = .90 2.90
These effects pull
ego toward the
extremes
The positive age b
pushes ego’s
behavior upward
Similarity pushes
ego to stay the
same
Altogether, the greatest contribution to the behavior function comes
from ego choosing to increase its behavior level
* For simplicity, calculations treat covariates as uncentered. However, SIENA will center dependent behaviors,
thus for actual computations replace raw values in calculations with centered values.
29. • Necessary for both network and behavior
• Determine the waiting time until actor’s chance to make decisions
• Function of observed changes
– But not the same as the number of changes observed
– Separate rate parameter for each period between observations
• Waiting time distributed uniformly by default, but differences can
be modeled based on:
• Actor attributes: do some types of actors experience more or
less change
• Degree: do actors with more/fewer ties experience a different
volume of change
May 17, 2019 Duke Social Networks & Health Workshop 29
Rate Functions
31. SAOM Estimation
• Goal during estimation is to identify parameter values (i.e., a model) that
produce networks whose statistics are centered on target statistics
– Same as modeled effects measured at t1+
• Robbins-Monro algorithm in three phases
1. Initialize parameter starting values
2. Use simulations to refine parameter estimates (next slide)
• A large number of simulation iterations, nested in 4+ subphases
• Actor decisions and timing based on objective and rate functions
• Update parameter estimates after each simulation iteration
– Attempt to minimize deviation of ending state from target
3. Additional simulations (2,000+) to calculate standard errors based on
parameter estimates from phase 2
May 17, 2019 Duke Social Networks & Health Workshop 31
32. Markov Chain Algorithm
May 17, 2019 Duke Social Networks & Health Workshop 32
Initialize at first observation
Actors draw:
1) Waiting time for network
2) Waiting time for behavior
Determined by rate functions
Shortest waiting time/type identified
Time up?
Actor changes tie|behavior
Determined by
objective functions
Update time
(next micro step)
“STOP”
YesNo
Begin Markov chain
Max
iterations?
No
Yes
If Phase 2,
update
parameters
Store ending network
& behavior
33. Post-Estimation 1
• Check for Convergence
• Convergence achieved when model is able to reproduce
observed network & behavior at time 2+
– For each effect, t-ratio to compare target statistics with
distribution (t should be < .10)
– Maximum t-ratio for convergence (tconv.max) should be
less than .25
– If convergence not reached, rerun with using estimates as
new starting values; may need to respecify model
May 17, 2019 Duke Social Networks & Health Workshop 33
34. Post-Estimation 2
• Goodness of Fit
• Use simulations to compare networks generated by model to
statistics NOT explicitly in the model
– Typical candidates:
• In- & Out-degree distributions
• Triad Census
• Geodesic distribution
• Behavior distribution
• Behavior network associations
May 17, 2019 Duke Social Networks & Health Workshop 34
36. Method
• National Longitudinal Study of Adolescent Health (Add Health)
• In-school + two in-home surveys, 1994-1996 (3 waves)
– Two schools provide most suitable data for longitudinal
network modeling; begin with Jefferson High
• Students nominated up to 5 male and 5 female friends
(directed network)
– Friendships coded present (1) or absent (0) for each dyad
• SAOM of friendship and smoking change
May 17, 2019 Duke Social Networks & Health Workshop 36
37. • Helpful to imagine the network function as a logistic regression
– Unit of analysis: dyad
– Outcome: tie presence (keeping or adding) vs. absence
(dissolving or failing to add)
– Effects represent how a one-unit change in the statistic affects
the log-odds of a tie during a microstep, all else being equal
• Some effects interpretable using odds ratios, but
– One-unit changes may not be meaningful
– All else is rarely equal (e.g., adding a tie affects the
outdegree count, at a minimum)
• Behavior function specifies how a one-unit change in the statistic
affects the log odds of adopting behavior at level z+1 vs. level z
May 17, 2019 Duke Social Networks & Health Workshop 37
A Note on Interpreting Coefficients
38. Network function b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transitive 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 17, 2019 Duke Social Networks & Health Workshop 38
Rate: Each actor is given ~10
micro steps in which to make a
change to its network
• Add a tie, drop a tie, or make
no change
Rate
39. Network function b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transitive 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 17, 2019 Duke Social Networks & Health Workshop 39
Outdegree: The negative sign is
typical. It means that ties are
unlikely, unless other effects in
the model make a positive
contribution to the network
function.
• Odds of adding a tie are .02
times (exp[-3.91]) the odds of
not adding a tie, all else being
equal
density
40. Network function b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transitive 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 17, 2019 Duke Social Networks & Health Workshop 40
Reciprocity: Ties that create a
reciprocated tie are more likely to
be added or maintained. This
effect hovers around 2 in
friendship-type network.
• Odds of adding/keeping a
reciprocated tie are 6.7 times
(exp[1.91]) the odds of
adding/keeping a non-
reciprocated tie, all else being
equal
recip
41. Network function b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transitive 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 17, 2019 Duke Social Networks & Health Workshop 41
Transitive triplets: Ties that
create more transitive triads
have a greater likelihood.
• Creating one additinoal
transitive triad increases the
odds of adding or keeping a
tie by 1.68 (exp[.52]), all else
being equal
• Should also include
interaction with reciprocity
(usually negative)
transTrip
42. Network function b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transitive 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 17, 2019 Duke Social Networks & Health Workshop 42
Indegree Popularity: Actors with
more incoming ties have a
greater likelihood of receiving
future ties
inPop
43. Network function b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transitive 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 17, 2019 Duke Social Networks & Health Workshop 43
Dyadic Covariate: Actors who
share an extracurricular activity
(coded 1) are more likely to have
a friendship tie
• A co-member is 1.32
(exp[.28]) times more likely to
be added than someone who
is not a co-member
X
44. Network function b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transitive 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 attractive 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 17, 2019 Duke Social Networks & Health Workshop 44
45. Network function b SE
Rate 10.26 *** .49
Outdegree -3.91 *** .08
Reciprocity 1.91 *** .09
Transitive 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 attractive as
friends than non-smokers.
Alter
Nonsmoker Smoker
Ego
Nonsmoker .25 -.19
Smoker -.51 .41
Similarity is an “interaction” between
ego and alter, thus interpretation
requires considering the main effects
• All else cannot be equal
Ego-alter selection: Contributions to
network objective function by dyad type
May 17, 2019 Duke Social Networks & Health Workshop 45
46. 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.
Smoking function b SE
Rate 2.06 *** .26
Linear shape -.11 .22
Quadratic shape 1.17 *** .16
Female .16 .19
Age -.00 .10
Parent Smoking .01 .23
Delinquency .44 ** .16
Alcohol -.10 .14
GPA -.09 .13
Average similarity 2.89 *** .91
In-degree -.04 .11
In-degree squared .00 .01
May 17, 2019 Duke Social Networks & Health Workshop 46
Rate: Students have around 2
chances on average (micro steps)
to change their smoking
behavior
Rate
47. 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.
Smoking function b SE
Rate 2.06 *** .26
Linear shape -.11 .22
Quadratic shape 1.17 *** .16
Female .16 .19
Age -.00 .10
Parent Smoking .01 .23
Delinquency .44 ** .16
Alcohol -.10 .14
GPA -.09 .13
Average similarity 2.89 *** .91
In-degree -.04 .11
In-degree squared .00 .01
May 17, 2019 Duke Social Networks & Health Workshop 47
linear
quad
48. 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.
Smoking function b SE
Rate 2.06 *** .26
Linear shape -.11 .22
Quadratic shape 1.17 *** .16
Female .16 .19
Age -.00 .10
Parent Smoking .01 .23
Delinquency .44 ** .16
Alcohol -.10 .14
GPA -.09 .13
Average similarity 2.89 *** .91
In-degree -.04 .11
In-degree squared .00 .01
May 17, 2019 Duke Social Networks & Health Workshop 48
Smoking (z, M=.9) Linear Quad
Raw Centered b = -.11 b = 1.17 Sum
0 -.90 .099 .948 1.047
1 .10 -.011 .012 .001
2 1.10 -.121 1.416 1.295
Smoking Level
SummedEffects
In combination, the linear and
quad effects represent the U-
shaped smoking distribution.
• Kids either don’t smoke or
smoke 12+ days/month.
.0
.2
.4
.6
.8
1.0
1.2
1.4
0 1 2
Contribution to Behavior Function
+ =
+ =
+ =
49. 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.
Smoking function b SE
Rate 2.06 *** .26
Linear shape -.11 .22
Quadratic shape 1.17 *** .16
Female .16 .19
Age -.00 .10
Parent Smoking .01 .23
Delinquency .44 ** .16
Alcohol -.10 .14
GPA -.09 .13
Average similarity 2.89 *** .91
In-degree -.04 .11
In-degree squared .00 .01
Ego Covariate: Delinquency leads
to higher levels of smoking
• A one-unit increase in
delinquency increases the log
odds of smoking at level z+1
(vs. level z) by .44 plus the
corresponding contributions
from the linear and quadratic
effects (statistics that
necessarily change with z level)
May 17, 2019 Duke Social Networks & Health Workshop 49
effFrom
50. 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.
Smoking function b SE
Rate 2.06 *** .26
Linear shape -.11 .22
Quadratic shape 1.17 *** .16
Female .16 .19
Age -.00 .10
Parent Smoking .01 .23
Delinquency .44 ** .16
Alcohol -.10 .14
GPA -.09 .13
Average similarity 2.89 *** .91
In-degree -.04 .11
In-degree squared .00 .01
Average Similarity: Students
adopt smoking levels that bring
them closer to the average of
their friends
• Note: A one-unit change here
is from maximum dissimilarity
to maximum similarity
xi+
-1
xijj
å (simij
z
- simz
)
ji
ij
zz
sim
jiij zz max
May 17, 2019 Duke Social Networks & Health Workshop 50
avSim
51. • How well is the estimated model able to reproduce features
of the observed data that were not explicitly modeled?
– Network
• Degree distribution
• Geodesic distribution
• Triad census
– Behavior distribution
Lots of room to improve GOF measures, especially behavior
May 17, 2019 Duke Social Networks & Health Workshop 51
Goodness of Fit (GOF)
52. Cumulative Indegree Distribution
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 17, 2019 Duke Social Networks & Health Workshop 52
53. Geodesic Distribution
Goodness of Fit of GeodesicDistribution
p: 0.001
Statistic
1 2 3 4 5 6 7
1381
2795
5014
7772
10598
12081 11892
May 17, 2019 Duke Social Networks & Health Workshop 53
54. 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 17, 2019 Duke Social Networks & Health Workshop 54
55. Smoking Distribution
Goodness of Fit of BehaviorDistribution
p: 1
Statistic
0 1 2
222
98
182
May 17, 2019 Duke Social Networks & Health Workshop 55
56. SAOM Lab, Part 1
If you haven’t done so already:
• Download the script from box
• Install the RSiena library
– Type: install.packages("RSiena”)
May 17, 2019 Duke Social Networks & Health Workshop 56
57. 4. Extensions & Miscellany
May 17, 2019 Duke Social Networks & Health Workshop 57
58. Extensions to Basic Model
May 17, 2019 Duke Social Networks & Health Workshop 58
• interactions
• continuous behavior outcomes
• event history outcomes
• test increase vs. decrease in ties and/or behavior
• multiple behaviors
• multiple networks
• valued ties
• multilevel networks
• two mode networks
• simulations (test interventions)
• time heterogeneity
• ML, Bayes estimation
59. Asymmetric Peer Influence
• Implicit assumption that effects work the same for:
– Tie formation vs. dissolution
– Behavior increase vs. decrease
• Behavior change constraint unrealistic for smoking
– Physical/psychological dependence, social learning
• Relax this assumption and separate behavior function into:
• Creation function: only considers increases
• Maintenance function: only considers decreases
May 17, 2019 Duke Social Networks & Health Workshop 59
60. Contributions to the Smoking Function
Contribution
Prospective
Smoking
Nonsmoking Alters
J = Jefferson High School
S = Sunshine High School
From Haas, Steven A. and David R. Schaefer. 2014. “With a Little Help from My Friends? Asymmetrical Social Influence on
Adolescent Smoking Initiation and Cessation.” Journal of Health and Social Behavior, 55:126-143.
Smoking level with
greatest contribution
most likely to be
adopted (with caveat
that actors can only
move behavior one
level during a given
micro step)
-3-113
Current Smoking
Util.
0 1 2
J
J
J
S
S
S
A
-3-113
Current Smoking
Util.
0 1 2
J
J
J
S
S
S
B
-3-113
Current Smoking
Util.
0 1 2
J
J
J
S
S
S
C
-3-113
Util.
J
J
J
S
S
S
D
-3-113
Util.
J
J
J
S
S
S
E
-3-113
Util.
J
J
J
S
S
S
F
Contribution
Prospective
Smoking
Smoking Alters
-3-11
Current Smoking
Util.
0 1 2
J
J
J
S
S
S
-3-11
Current Smoking
Util.
0 1 2
J
J
J
S
S
S
-3-11
Util.
-3-113
Util.
0 1 2
J
J
J
S
S
S
G
-3-113
Util.
0 1 2
J
J
J
S
S
S
H
-3-113
Util.
Ego is currently a moderate smoker (1)
May 17, 2019 Duke Social Networks & Health Workshop 60
61. Contributions,
Cont.
J = Jefferson High School
S = Sunshine High School
From Haas, Steven A. and David R.
Schaefer. 2014. “With a Little Help
from My Friends? Asymmetrical
Social Influence on Adolescent
Smoking Initiation and Cessation.”
Journal of Health and Social
Behavior, 55:126-143.
Smoking level
with greatest
predicted
contribution is
most likely to be
adopted
May 17, 2019 Duke Social Networks & Health Workshop 61
Ego Current Smoking Status
Nonsmoking (0) Moderate (1) Smoking (2)
Prospective Smoking Level
PredictedContribution
Nonsmoking(0)
Friends’CurrentSmokingStatus
Moderate(1)Smoking(2)
-3-113
Current Smoking
0 1 2
J
J
J
S
S
S
-3-113
Current Smoking
Util.
0 1 2
J
J
J
S
S
S
-3-113
Current Smoking
Util.
0 1 2
J
J
J
S
S
S
-3-113
Current Smoking
0 1 2
J
J
J
S
S
S
-3-113
Current SmokingUtil.
0 1 2
J
J
J
S
S
S
-3-113
Current Smoking
Util.
0 1 2
J
J
J
S
S
S
-3-113
0 1 2
J
J
J
S
S
S
-3-113
Util.
0 1 2
J
J
J
S
S
S
-3-113
Util.
0 1 2
J
J
J
S
S
S
62. SIENA as an Agent-Based Model
• Model fitting uses a simulation algorithm, making computational
experiments a natural extension
• Decision rules are grounded in empirical estimates
• Useful to evaluate goodness-of-fit, decompose network-behavior
associations, and evaluate interventions
1. Fit model to empirical data (optional)
2. Simulate network evolution using estimated parameters or manipulations
of them
• Can also manipulate initial conditions (e.g., network structure,
behavior distribution, etc.)
3. Measure simulated network/behavior properties of interest
May 17, 2019 Duke Social Networks & Health Workshop 62
63. Decomposing Network Homogeneity
Source Selection (%) 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
selection vs. influence?
– Systematically set selection and influence parameters to
zero and simulate network-behavior co-evolution (see
Steglich et al. 2010)
May 17, 2019 Duke Social Networks & Health Workshop 63
64. Evaluating Interventions: How do smoking/friendship
dynamics affect smoking prevalence?
• Manipulate model parameters related to key “intervention
levers”
– Peer influence
– Smoker popularity
• Remaining model parameters from fitted model
• Initial conditions = observed wave 1 data
May 17, 2019 Duke Social Networks & Health Workshop 64
65. Manipulations
• Peer influence (avSim): observed b = 2.89
– Manipulated b = 0, 1, 2, 3, 4, 5, 6
• Absent to strong (rarely observe negative PI)
• Smoker popularity (altX): observed b = .14
– Manipulated b = -.45, -.3, -.15, 0, .15, .3, .45, .6, .75
• Unpopular…absent…popular
• 1,000 iterations per condition
• Remaining parameters same as fitted model
• Initial conditions: observed wave 1 data
May 17, 2019 Duke Social Networks & Health Workshop 65
66. Results of Manipulating Peer Influence (PI) and
Smoking-based Popularity (smoke alter)
Schaefer DR, adams j, Haas SA. 2013. Social Networks
and Smoking: Exploring the Effects of Peer Influence
and Smoker Popularity through Simulations.
Health Education & Behavior, 40(S1):24-32.
May 17, 2019 Duke Social Networks & Health Workshop 66
Independent Manipulations
Joint Manipulation
Stronger peer influence increases smoking
prevalence, but only when smokers are
popular (negative effects when smokers
unpopular)
67. Context Effects:
• We’ve only been considering limited school contexts
– Jefferson High is somewhat atypical
May 17, 2019 Duke Social Networks & Health Workshop 67
68. Smoking Prevalence
Smoking Prevalence across 95 Add Health schools
(proportion of students who smoked in past 12 months)
May 17, 2019 Duke Social Networks & Health Workshop 68
Jefferson High
69. Context Effects:
• We’ve only been considering limited school contexts
– Jefferson High is somewhat atypical
• How do the effects of these manipulations depend upon
context?
– Perform the same computational experiment, but in
schools with higher or lower prevalence
May 17, 2019 Duke Social Networks & Health Workshop 69
70. Vary Initial Conditions
• Key factor: initial smoking prevalence
• But how to distribute smoking, given its
association with network structure?
– Associations depend on smoking prevalence
May 17, 2019 Duke Social Networks & Health Workshop 70
.25 .35 .45 .55 .65 .75
Prevalence
0 1 2
010203040506070
0 1 2
010203040506070
0 1 2
010203040506070
0 1 2
010203040506070
0 1 2
010203040506070
0 1 2
010203040506070
71. May 17, 2019 Duke Social Networks & Health Workshop 71
72. May 17, 2019 Duke Social Networks & Health Workshop 72
73. Prevalence Manipulation
1. Randomly assign smoking values
2. Model-based: Use ERGM to simulate initial networks with
specified network properties
– Network/smoking associations
– Autocorrelation on factors related to smoking (sex, age,
alcohol, smoking)
– Retain networks matching observed on density,
autocorrelation, and smoker popularity
3. Empirically-based: use each observed Add Health school
– Maintains all associations
May 17, 2019 Duke Social Networks & Health Workshop 73
74. Results: 95 Add Health Schools as Initial Conditions
Observed Estimates
Smoker Popularity
b = .2
Peer Influence
b = 3
May 17, 2019 Duke Social Networks & Health Workshop 75
ChangeinPrevelance
Initial Prevalence
●
.2 .3 .4 .5
−.2−.10.1.2
75. May 17, 2019 Duke Social Networks & Health Workshop 76
Smoking Distribution: Empirically-Based, Model-Based, Random
77. • Ties are more or less enduring states
– Plausible for friendship or collaborations
– Not useful for “event” data (e.g. phone calls)
• Change occurs in continuous time
• Markov process: future state only a function of current state
– No lagged effects or “grudges”
• Actors control outgoing ties and behavior
• One change at a time
– No coordinated or simultaneous changes
May 17, 2019 Duke Social Networks & Health Workshop 78
SIENA Assumptions
78. • Up to 10% probably ok, more than 20% likely a problem
• Endogenous network & behavior imputation
– 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-informative, thus not used
in calculating target statistics
– Convergence and fit are determined based only upon
observed cases
May 17, 2019 Duke Social Networks & Health Workshop 79
Missing Data
79. Good Sources of Information
May 17, 2019 Duke Social Networks & Health Workshop 80
• RSiena manual
• Snijders, van de Bunt & Steglich, 2010
• Steglich, Snijders & Pearson, 2010
• Tom Snijders’ SIENA website
www.stats.ox.ac.uk/siena/
– Workshops
– Scripts
– Applications in the literature
– Latest version of RSiena
– Link to stocnet listserv – important updates announced here
– “Siena_algorithms.pdf”
80. SAOM Lab, Part 2
May 17, 2019 Duke Social Networks & Health Workshop 81
81. May 17, 2019 Duke Social Networks & Health Workshop 82
Data structures possible for T>1 time points
(SIENA data creation functions in parentheses)
Exogenous Endogenous
Data Type
Single
time point
Multiple time
points (1 to T-1)
Multiple time
points (1 to T)
Individual
Attribute
Constant Covariate
(coCovar)
Changing Covariate
(varCovar)
Dependent Behavior
(sienaDependent,
type=‘behavior’)
Network*
Dyadic Covariate
(coDyadCovar)
Changing Dyadic
Covariate
(varDyadCovar)
Dependent Network
(sienaDependent,
type=‘oneMode’)
* Bipartite networks also possible
• A model can have multiple instances of each type (even
dependent networks and behaviors)
Composition Change is 7th type of object
82. • One mode or two mode network with at least two
observations, 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 / (ties formed + ties dissolved + ties persisted)
• “Complete network data” all actors w/in bounded setting
– 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 composition change
• Recommended N: 30-2000
May 17, 2019 Duke Social Networks & Health Workshop 83
Data Structure: Dependent Network
83. • Dependent behaviors
– Time-varying attributes used as dependent variable(s)
– Coded as integer (e.g., 1-10)
– Last time point is used
• Changing actor covariates
– Time-varying attributes used as independent variables
– Last time point not used (only applicable for 3+ waves)
• Constant covariates
– Ex: age, sex, race/ethnicity, behavior
• Dyadic covariates
– Ex: settings that drive contact
NOTE: Covariates are centered by default
May 17, 2019 Duke Social Networks & Health Workshop 84
Additional Data Structures
84. Time 1 Time 2
Mutuality
Count But…
SIENA Treatment
During Estimation
+ 2 Both explained
by stability term
Counts as keeping 2
+ 2 One explained
by stability term
Counts as 2 (add 1 &
~keep 1)
NULL
+ 2 Both count Counts as 2, only 1
via recip. (2 steps)
+ 0 Not reciprocity? Counts as 0, but
could add + drop
+ 0 +1 mutual tie
-1 mutual tie
Model respects loss
of 1
NULL
+ 0 Lost 2! Model respects loss
of 2 (2 steps)
85May 17, 2019 Duke Social Networks & Health Workshop
Assumes ERGM with edges, stability, and mutuality terms
TERGM Dependence: Mutuality Example
Editor's Notes
We observe homophily at time t, which is consistent with peer influence, but this could also be due to friend selection.
Not shown: could also be selecting into a common context creates homogeneity
We observe a popular smoker at time t, but what preceded it?
New model specification
Network and Behavior are both outcomes in the model.
1. Rate functions determine
Which actor makes a change
Whether change is to network or behavior
2. Objective functions determine which change is made
Consider all possible ties/behavior change
Make change that maximizes objective function
Update STOP:
During phase 2, update parameters, go to next iteration
During phase 3, just go to next iteration
Fitted Model
Fitted Model
Fitted Model
Fitted Model
Fitted Model
Fitted Model
Fitted Model
Fitted Model
Fitted Model
Fitted Model
Fitted Model
Fitted Model
Fitted Model
New specification of AJPH model
New model specification
New model specification
New model specification
New model specification
Implication: students don’t begin smoking unless friends do. Students may cease smoking even if friends continue smoking.
Implication: students don’t begin smoking unless friends do. Students may cease smoking even if friends continue smoking.
Peer influence affects prevalence, direction depends upon whether smokers are popular or unpopular.
Popularity affects prevalence, but only when PI is present.
Not clear how generalizable the simulation results are.
More clustering in higher prevalence schools
Middle school: smokers less popular in higher prevalence schools
High school: smokers more popular in higher prevalence schools
When smokers are unpopular, increasing PI decreases prevalence in all but highest smoking contexts
When smokers are popular, increasing PI magnifies existing trends: low-prevalence contexts exhibit decreases while high-prevalence contexts exhibit increases.
Observe that high/low cutoff shifts with smoker popularity. Implication: if smokers are popular then even low prevalence schools can experience increases via PI.
Using model parameters estimated from observed school (Black=Observed, Red=Random, Blue=ERGM)
When smokers are unpopular, increasing PI decreases prevalence in all but highest smoking contexts
When smokers are popular, increasing PI magnifies existing trends: low-prevalence contexts exhibit decreases while high-prevalence contexts exhibit increases.
Observe that high/low cutoff shifts with smoker popularity. Implication: if smokers are popular then even low prevalence schools can experience increases via PI.
Results with random smoking distribution.
In low prevalence context, peer influence is a good things. Serves a protective function.
In high prevalence context, peer influence leads to trouble.