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**An introduction to exponential random graph models (ERGM)**

The Social Statistics Discipline Area, School of Social Sciences An introduction to exponential random graph models (ERGM) Johan Koskinen Mitchell Centre for Network Analysis Workshop: Monday, 29 August 2011

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Part 1a Why an ERGM

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**Networks matter – ERGMS matter**

FEMALE Male 6018 grade 6 children 1966

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**Networks matter – ERGMS matter**

6018 grade 6 children 1966 – 300 schools Stockholm

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**Networks matter – ERGMS matter**

6018 grade 6 children 1966 – 200 schools Stockholm Koskinen and Stenberg (in press) JEBS

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**Networks matter – ERGMS matter**

6018 grade 6 children 1966 – 200 schools Stockholm Koskinen and Stenberg (in press) JEBS

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**Networks matter – ERGMS matter**

6018 grade 6 children 1966 – 200 schools Stockholm Koskinen and Stenberg (in press) JEBS

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**Networks matter – ERGMS matter**

6018 grade 6 children 1966 – 200 schools Stockholm Koskinen and Stenberg (in press) JEBS

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Part 1b Modelling graphs

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**Notational preliminaries Why and what is an ERGM Dependencies **

Social Networks Notational preliminaries Why and what is an ERGM Dependencies Estimation Geyer-Thompson Robins-Monro Bayes (The issue, Moller et al, LISA, exchange algorithm) Interpretation of effects Convergence and GOF Further issues

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**Numerous recent substantively driven studies**

Networks matter – ERGMS matter Numerous recent substantively driven studies Gondal, The local and global structure of knowledge production in an emergent research field, SOCNET 2011 Lomi and Palotti, Relational collaboration among spatial multipoint competitors , SOCNET 2011 Wimmer & Lewis, Beyond and Below Racial Homophily, AJS 2010 Lusher, Masculinity, educational achievement and social status, GENDER & EDUCATION 2011 Rank et al. (2010). Structural logic of intra-organizational networks, ORG SCI, 2010. Book for applied researchers: Lusher, Koskinen, Robins ERGMs for SN, CUP, 2011

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**The exponential random graph model (p*) framework**

An ERGM (p*) model is a statistical model for the ties in a network Independent (pairs of) ties (p1, Holland and Leinhardt, 1981; Fienberg and Wasserman, 1979, 1981) Markov graphs (Frank and Strauss, 1986) Extensions (Pattison & Wasserman, 1999; Robins, Pattison & Wasserman, 1999; Wasserman & Pattison, 1996) New specifications (Snijders et al., 2006; Hunter & Handcock, 2006)

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**ERGMS – modelling graphs**

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**ERGMS – modelling graphs**

We want to model tie variables But structure – overall pattern – is evident What kind of structural elements can we incorporate in the model for the tie variables?

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**the number of ties in our model**

ERGMS – modelling graphs If we believe that the frequency of interaction/density is an important aspect of the network i l j k We should include Counts of the number of ties in our model

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**the number of mutual ties in our model**

ERGMS – modelling graphs If we believe that the reciprocity is an important aspect of the (directed) network i l j k We should include Counts of the number of mutual ties in our model

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**ERGMS – modelling graphs**

If we believe that an important aspect of the network is that k two edge indicators {i,j} and {i’,k} are conditionally dependent if {i,j} {i’,k} i j We should include counts of degree distribution; preferential attachment, etc friends meet through friends; clustering; etc

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**ERGMS – modelling graphs**

If we believe that the attributes of the actors are important (selection effects, homophily, etc) We should include counts of Heterophily/homophily Distance/similarity

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**ERGMS – modelling graphs**

If we believe that (Snijders, et al., 2006) i k two edge indicators {i,k} and {l,j} are conditionally dependent if {i,l} , {l,j} E l j clustered regions

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** + 2 = ERGMS – modelling graphs Pr given the rest log Pr**

k log Pr given the rest i k j k m n 1 # +2 # +3 # + + # i l i + 2 = adding edge, e.g.: k j k

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**ERGMS – modelling graphs**

The conditional formulation Pr given the rest i k log Pr given the rest i k where Is the difference in counts of structure type k May be ”aggregated” for all dyads so that the model for the entire adjacency matrix can be written

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**ERGMS – modelling graphs**

For a model with edges and triangles The model for the adjacency matrix X is a weighted sum where The parameters 1 and weight the relative importance of ties and triangles, respectively - graphs with many triangles but not too dense are more probable than dense graphs with few triangles

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**ERGMS – modelling graphs: example**

Padgett’s Florentine families (Padgett and Ansell, 1993) network of 8 employees in a business company BusyNet <- as.matrix(read.table( ”PADGB.txt”,header=FALSE))

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**ERGMS – modelling graphs: example**

BusyNetNet <- network(BusyNet, directed=FALSE) plot(BusyNetNet) Requires libraries 'sna’,'network’

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**ERGMS – modelling graphs: example**

Observed and Density:

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**ERGMS – modelling graphs: example**

For a model with only edges Equivalent with the model: For each pair flip a p-coin heads tails Where p is the probability coin comes up heads

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**ERGMS – modelling graphs: example**

The (Maximul likelihood) estimate of p is the density here: i.e., an estimated one in 8 pairs establish a tie For an ERGM model with edges and hence

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**ERGMS – modelling graphs: example**

Solving for 1, we have that the density parameter and for the MLE Let’s check in stanet

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**approx. standard error of MLE of 1,**

ERGMS – modelling graphs: example Estim1 <- ergm(BusyNetNet ~ edges) summary(Estim1) approx. standard error of MLE of 1, Parameter corresponding to Success:

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**ERGMS – modelling graphs: example**

Do we believe in the model: For each pair flip a p-coin heads tails Let’s fit a model that takes Markov dependencies into account where statnet

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**approx. standard error of MLE of ,**

ERGMS – modelling graphs: example Estim2 <- ergm(BusyNetNet ~ kstar(1:3) + triangles) summary(Estim2) approx. standard error of MLE of ,

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Part 3 Estimation

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**Sum over all 2n(n-1)/2 graphs**

Likelihood equations for exponential fam ”Aggregated” to a joint model for entire adjacency matrix X Sum over all 2n(n-1)/2 graphs The MLE solves the equation (cf. Lehmann, 1983):

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**Likelihood equations for exponential fam**

Solving Using the cumulant generating function (Corander, Dahmström, and Dahmström, 1998) Stochastic approximation (Snijders, 2002, based on Robbins-Monro, 1951) Importance sampling (Handcock, 2003; Hunter and Handcock, 2006, based on Geyer-Thompson 1992)

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**- Initialisation phase - Main estimation **

Robbins-Monro algorithm Solving Snijders, 2002, algorithm - Initialisation phase - Main estimation - convergence check and cal. of standard errors MAIN: Draw using MCMC

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**Robbins-Monro algorithm**

Parameter space Network space Observed data In this case, the distribution of graphs from the parameter values is not consistent with the observed data A set of parameter values We can simulate the distribution of graphs associated with any given set of parameters.

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**Robbins-Monro algorithm**

Parameter space Network space We want to find the parameter estimates for which the observed graph is central in the distribution of graphs

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**Robbins-Monro algorithm**

Parameter space Network space Observed data Based on this discrepancy, adjust parameter values So: 1. compare distribution of graphs with observed data 2. adjust parameter values slightly to get the distribution closer to the observed data 3. keep doing this until the estimates converge to the right point

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**Robbins-Monro algorithm**

Parameter space Network space Over many iterations we walk through the parameter space until we find the right estimates

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**Phase 1, Initialisation phase **

Robbins-Monro algorithm Phase 1, Initialisation phase Find good values of the initial parameter state And the scaling matrix (use the score-based method, Schweinberger & Snijders, 2006)

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**Phase 2, Main estimation phase Iteratively update θ**

Robbins-Monro algorithm Phase 2, Main estimation phase Iteratively update θ by drawing one realisation from the model defined by the current θ(m) Repeated in sub-phases with fixed

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**Phase 2, Main estimation phase **

Robbins-Monro algorithm Phase 2, Main estimation phase Relies on us being able to draw one realisation from the ERGM defined by the current θ We can NOT do this directly We have to simulate x More specifically use Markov chain Monte Carlo

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**What do we need to know about MCMC? Method: **

Robbins-Monro algorithm What do we need to know about MCMC? Method: Generate a sequence of graphs for arbitrary x(0), using an updating rule… so that as

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**What do we need to know about MCMC? **

Robbins-Monro algorithm What do we need to know about MCMC? So if we generate an infinite number of graphs in the “right” way we have the ONE draw we need to update θ once? Typically we can’t wait an infinite amount of time so we settle for multiplication factor In Pnet very large is

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**Phase 3, Convergence check and calculating standard errors **

Robbins-Monro algorithm Phase 3, Convergence check and calculating standard errors At the end of phase 2 we always get a value But is it the MLE? Does it satisfy ?

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**Phase 3, Convergence check and calculating standard errors **

Robbins-Monro algorithm Phase 3, Convergence check and calculating standard errors Phase 3 simulates a large number of graphs And checks if A minor discrepancy - due to numerical inaccuracy - is acceptable Convergence statistics:

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**Approximated using importance sample from MCMC**

Geyer-Thompson Solving Handcock, 2003, approximate Fisher scoring MAIN: Approximated using importance sample from MCMC

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**Sum over all 2n(n-1)/2 graphs**

Bayes: dealing with likelihood The normalising constant of the posterior not essential for Bayesian inference, all we need is: … but Sum over all 2n(n-1)/2 graphs

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Bayes: MCMC? Consequently, in e.g. Metropolis-Hastings, acceptance probability of move to θ … which contains

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**Bayes: Linked Importance Sampler Auxiliary Variable MCMC**

LISA (Koskinen, 2008; Koskinen, Robins & Pattison, 2010): Based on Møller et al. (2006), we define an auxiliary variable And produce draws from the joint posterior using the proposal distributions and

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**Bayes: alternative auxiliary variable**

LISA (Koskinen, 2008; Koskinen, Robins & Pattison, 2010): Based on Møller et al. (2006), we define an auxiliary variable Many linked chains: Computation time storage (memory and time issues) Improvement: use exchange algorithm (Murray et al. 2006) and Accept θ* with log-probability: Caimo & Friel, 2011

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**Storing only parameters **

Bayes: Implications of using alternative auxiliary variable Storing only parameters No pre tuning – no need for good initial values Standard MCMC properties of sampler Less sensitive to near degeneracy in estimation Easier than anything else to implement QUICK and ROBUST Improvement: use exchange algorithm (Murray et al. 2006) and Accept θ* with log-probability: Caimo & Friel, 2011

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**Interpretation of effects**

Part 4 Interpretation of effects

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**Markov dependence assumption:**

Problem with Markov models Markov dependence assumption: k two edge indicators {i,j} and {i’,k} are conditionally dependent if {i,j} {i’,k} i j We have shown that the only effects are: degree distribution; preferential attachment, etc friends meet through friends; clustering; etc

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**hard Often for Markov model Matching or Problem with Markov models**

degree distribution; preferential attachment, etc friends meet through friends; clustering; etc Matching Impossible! hard or

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**hard Matching or Some statistic k or Problem with Markov models**

Impossible! hard or # triangles Some statistic k or # edges

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**hard Matching Problem with Markov models # triangles**

The number of triangles for a Markov model with edges (-3), 2-stars (.5), 3-stars (-.2) and triangles (1.1524) # triangles

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**hard Matching Problem with Markov models**

Exp. # triangles and edges for a Markov model with edges, 2-stars, 3-stars and triangles

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**Matching If for some statistic k Implies: Problem with Markov models**

Impossible! Matching If for some statistic k Implies: Similarly for max (or conditional min/max) e.g. A graph on 7 vertices with 5 edges # 2-stars # edges

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**First solutions (Snijders et al., 2006)**

Problem with Markov models First solutions (Snijders et al., 2006) Markov: adding k-star adds (k-1) stars alternating sign compensates but eventually zk(x)=0 Alternating stars: Restriction on star parameters - Alternating sign prevents explosion, and models degree distribution

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**First solutions (Snijders et al., 2006)**

Problem with Markov models First solutions (Snijders et al., 2006) Markov: adding k-star adds (k-1) stars alternating sign compensates but eventually zk(x)=0

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**First solutions (Snijders et al., 2006)**

Problem with Markov models First solutions (Snijders et al., 2006) Include all stars but restrict parameter: new Alternating stars: Restriction on star parameters - Alternating sign prevents explosion, and models degree distribution

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**First solutions (Snijders et al., 2006)**

Problem with Markov models First solutions (Snijders et al., 2006) Include all stars but restrict parameters: new Expressed in terms of degree distribution

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**First solutions (Snijders et al., 2006)**

Problem with Markov models First solutions (Snijders et al., 2006) Include all stars but restrict parameters: Interpretation: Positive parameter (λ ≥ 1) – graphs with some high degree nodes and larger degree variance more likely than graphs with more homogenous degree distribution Negative parameter (λ ≥ 1) – the converse…

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**First solutions (Snijders et al., 2006)**

Problem with Markov models First solutions (Snijders et al., 2006) Markov: triangles evenly spread out but one edge can add many triangles… Alternating triangles: Restrictions on different order triangles – alternating sign Prevents explosion, and Models multiply clustered regions Social circuit dependence assumption

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**First solutions (Snijders et al., 2006)**

Problem with Markov models First solutions (Snijders et al., 2006) Markov: triangles evenly spread out but one edge can add many triangles… k-triangles: 1-triangles: 2-triangles: 3-triangles:

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**First solutions Problem with Markov models Weigh the k-triangles:**

Where: Alternating triangles: Restrictions on different order triangles – alternating sign Prevents explosion, and Models multiply clustered regions

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**First solutions Problem with Markov models Underlying assumption:**

Social circuit dependence assumption l j two edge indicators {i,k} and {l,j} are conditionally dependent if {i,l} , {l,j} E Alternating triangles: Restrictions on different order triangles – alternating sign Prevents explosion, and Models multiply clustered regions

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**Alternative interpretation**

Problem with Markov models Alternative interpretation We may (geometrically) weight together : i j

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**Alternative intrepretation**

Problem with Markov models Alternative intrepretation We may also define the Edgewise Shared Partner Statistic: … and we can weigh together the ESP statistics using Geometrically decreasing weights: GWESP i j

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**Example Lazega’s law firm partners**

Part 4b Example Lazega’s law firm partners

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**Boston office: Hartford office: Providence off.:**

Lazega’s (2001) Lawyers Bayesian Data Augmentation Collaboration network among 36 lawyers in a New England law firm (Lazega, 2001) Boston office: Hartford office: Providence off.: least senior: most senior:

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**Bayesian Data Augmentation**

Lazega’s (2001) Lawyers Bayesian Data Augmentation Fit a model with ”new specifications” and covariates Edges: Seniority: Practice: Homophily Sex: Office: GWESP: Main effect

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**PNet: Lazega’s (2001) Lawyers Bayesian Data Augmentation**

Fit a model with ”new specifications” and covariates PNet:

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**Bayesian Data Augmentation**

Lazega’s (2001) Lawyers Bayesian Data Augmentation Fit a model with ”new specifications” and covariates Main effect Edges: Seniority: Practice: Homophily Practice: Sex: Office: GWESP:

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**Interpreting attribute-related effects**

Part 4c Interpreting attribute-related effects

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**Fitting an ERGM in Pnet: a business communications network **

Bayesian Data Augmentation For ”wwbusiness.txt” we have recorded wheather the employee works in the central office or is a traveling sales represenative office sales Rb for attribute R for attribute

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**Fitting an ERGM in Pnet: a business communications network **

Bayesian Data Augmentation Consider a dyad-independent model office sales Rb for attribute R for attribute

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**Fitting an ERGM in Pnet: a business communications network **

Bayesian Data Augmentation With log odds office sales Rb for attribute R for attribute

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**Interpreting higher order effects**

Part 4d Interpreting higher order effects

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**Alternating stars a way of **

Unpacking the alternating star effect Alternating stars a way of “fixing” the Markov problems (models all degrees) Controlling for paths in clustering 1-triangles: 2-triangles: 3-triangles: k-triangles measure clustering…

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**Alternating stars a way of **

Unpacking the alternating star effect Alternating stars a way of “fixing” the Markov problems (models all degrees) Controlling for paths in clustering 1-triangles: 2-triangles: 3-triangles: Is it closure or an artefact of many stars/2-paths?

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**Interpreting the alternating star parameter:**

Unpacking the alternating star effect Interpreting the alternating star parameter: centralization – + variance of degree measure centralization

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**alternating star parameter**

Unpacking the alternating star effect Interpreting the alternating star parameter: centralization – + alternating star parameter – +

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**alternating star parameter**

Unpacking the alternating star effect Statistics for graph (n = 16); fixed density; alt trian: 1.17 alternating star parameter – +

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**alternating star parameter**

Unpacking the alternating star effect Statistics for graph (n = 16); fixed density; alt trian: 1.17 alternating star parameter – +

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**alternating star parameter**

Unpacking the alternating star effect Graphs (n = 16); fixed density; alt trian: 1.17 alternating star parameter – +

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**Note also the influence of isolates:**

Problem with Markov models Note also the influence of isolates: Alt, stars in terms of degree distribution

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**alternating star parameter**

Problem with Markov models Note also the influence of isolates: alternating star parameter – +

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**Note also the influence of isolates:**

Problem with Markov models Note also the influence of isolates: This is because we impose a particular shape on the degree distribution

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**alternating star parameter**

Problem with Markov models The degree distribution for different values of: – alternating star parameter +

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**Alternating triangles measure clustering**

Unpacking the alternating triangle effect Alternating triangles measure clustering We may also define the Edgewise Shared Partner Statistic: … and we can weigh together the ESP statistics using Geometrically decreasing weights: GWESP i j

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**alternating Triangle parameter**

Unpacking the alternating triangle effect Statistics for graph (n = 16); fixed density; alt star: 2.37 alternating Triangle parameter – +

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**alternating Triangle parameter**

Unpacking the alternating triangle effect Statistics for graph (n = 16); fixed density; alt star: 2.37 alternating Triangle parameter – +

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**alternating Triangle parameter**

Unpacking the alternating triangle effect Statistics for graph (n = 16); fixed density; alt star: 2.37 + closed open clustering alternating Triangle parameter – +

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**alternating Triangle parameter**

Unpacking the alternating triangle effect Statistics for graph (n = 16); fixed density; alt star: 2.37 + closed open clustering alternating Triangle parameter – +

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**Alternating triangles model multiply clustered areas**

Unpacking the alternating triangle effect Alternating triangles model multiply clustered areas For multiply clustered areas triangles stick together We model how many others tied actors have Edgewise Shared Partner Statistic: i j

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**Statistics for graph (n = 16); fixed density; alt star: 2.37 +**

Unpacking the alternating triangle effect Statistics for graph (n = 16); fixed density; alt star: 2.37 + Alternating triangle para # edwise shared partners – +

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**Convergence check and goodness of fit**

Part 5 Convergence check and goodness of fit

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**Revisiting the Florentine families**

statnet Why difference? PNet

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**Pnet checks convergence in 3rd phase**

Revisiting the Florentine families Pnet checks convergence in 3rd phase

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**Revisiting the Florentine families**

Lets check For statnet

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**Estim3 <- ergm(BusyNetNet ~ kstar(1:3) + triangles , verbose=TRUE)**

mcmc.diagnostics(Estim3) ?

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**Simulation, GOF, and Problems**

Given a specific model we can simulate potential outcomes under that model. This is used for Estimation: (a) to match observed statistics and expected statistics (b) to check that we have “the solution” GOF: to check whether the model can replicated features of the data that we not explicitly modelled Investigate behaviour of model, e.g.: degeneracy and dependence on scale

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**For fitted effects this is true by construction **

Simulation, GOF, and Problems Standard goodness of fit procedures are not valid for ERGMs – no F-tests or Chi-square tests available If indeed the fitted model adequately describes the data generating process, then the graphs that the model produces should be similar to observed data For fitted effects this is true by construction For effects/structural feature that are not fitted this may not be true If it is true, the modelled effects are the only effects necessary to produce data – a “proof” of the concept or ERGMs

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**Example: for our fitted model for Lazega**

Simulation, GOF, and Problems Example: for our fitted model for Lazega We can look at how well the model reproduces For arbitrary function g

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**From the goodness-of-fit tab in Pnet we get**

Simulation, GOF, and Problems From the goodness-of-fit tab in Pnet we get

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**Simulation, GOF, and Problems**

Effect MLE s.e. Density -6.501 0.727 Main effect seniority 1.594 0.324 Main effect practice 0.902 0.163 Homophily effect practice 0.879 0.231 Homophily sex 1.129 0.349 Homophily office 1.654 0.254

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**Simulation, GOF, and Problems**

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**Simulation, GOF, and Problems**

Effect MLE s.e. Density -6.501 0.727 -6.510 0.637 Main effect seniority 1.594 0.324 0.855 0.235 Main effect practice 0.902 0.163 0.410 0.118 Homophily effect practice 0.879 0.231 0.759 0.194 Homophily sex 1.129 0.349 0.704 0.254 Homophily office 1.654 1.146 0.195 GWEPS 0.897 0.304 Log-lambda 0.778 0.215

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**Simulation, GOF, and Problems**

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Part 2 Dependencies

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**Independence - Deriving the ERGM**

heads i l i l tails j k i l i m n heads k tails i k

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**does not help us predict SEK**

Independence - Deriving the ERGM AUD 0.5 SEK 0.5 0.25 l i k l i k Knowledge of AUD, e.g. does not help us predict SEK e.g. whether or

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**does not help us predict SEK**

Independence - Deriving the ERGM Knowledge of AUD, e.g. does not help us predict SEK e.g. whether or even though dyad {i,l} l i i and dyad {i,k} k have vertex i i in common

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**Independence - Deriving the ERGM**

May we find model such that knowledge of AUD, e.g. does help us predict SEK e.g. whether or ? AUD 0.5 i l SEK 0.5 0.4 0.1 k i l k

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**Consider the tie-variables that have Mary in common**

Deriving the ERGM: From Markov graph to Dependence graph Consider the tie-variables that have Mary in common paul mary john pete How may we make these “dependent”?

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul mary mary pete john pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul mary mary pete mary john john pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul mary paul mary mary pete mary john john pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul mary paul mary mary pete mary john john pete pete john

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul mary paul paul john mary mary pete mary john john pete pete john

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul mary paul paul paul john pete mary mary pete mary john john pete pete john

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**Deriving the ERGM: From Markov graph to Dependence graph**

The “probability structure” of a Markov graph is described by cliques of the dependence graph (Hammersley-Clifford)…. paul m,pa pa,pe pa,j mary m,pe m,j john pe,j pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul m,pa pa,pe pa,j mary m,pe m,j pe,j pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul mary m,pe john pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul m,pa pa,pe pa,j mary m,pe m,j john pe,j pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul m,pa pa,pe pa,j mary m,pe m,j john pe,j pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul mary m,pe m,j john pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul m,pa pa,pe pa,j mary m,pe m,j john pe,j pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul m,pa pa,pe pa,j mary m,pe m,j john pe,j pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul mary m,pe m,j john pe,j pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul m,pa pa,pe pa,j mary m,pe m,j john pe,j pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul m,pa pa,pe pa,j mary m,pe m,j john pe,j pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul m,pa pa,j mary m,pe john pe,j pete

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**Deriving the ERGM: From Markov graph to Dependence graph**

paul m,pa pa,pe pa,j mary m,pe m,j john pe,j pete

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**too many statistics (parameters)**

From Markov graph to Dependence graph – distinct subgraphs? too many statistics (parameters)

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**The homogeneity assumption**

= = = =

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**Interaction terms in log-linear model of types**

A log-linear model (ERGM) for ties ”Aggregated” to a joint model for entire adjacency matrix Interaction terms in log-linear model of types

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**More than is explained by margins**

A log-linear model (ERGM) for ties By definition of (in-) dependence More than is explained by margins E.g. and co-occuring i j k Main effects interaction term

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**Summary of fitting routine**

Part 7 Summary of fitting routine

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**rerun if model not converged include more parameters? GOF **

The steps of fitting an ERGM fit base-line model check convergence rerun if model not converged include more parameters? GOF candidate models

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Part 6 Bipartite data

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**Bi-partite networks: cocitation (Small 1973)**

2 3 4 5 7 6 texts cite

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**Bi-partite networks: cooffending (. Sarnecki, 2001)**

offences offenders participating

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**Bi-partite networks people people directors participating in**

Social events (Breiger, 1974) people belonging to voluntary organisations (Bonachich, 1978) sitting on corporate boards (eg. Mizruchi, 1982 directors

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**Tie: If two directors share a board**

One-mode projection Two-mode Tie: If two directors share a board one-mode

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**Bi-partite networks texts cite 2 2 2 2 2 2 3 3 3 3 6 7 1 1 1 1 1 1 4 4**

5 texts cite

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Bi-partite networks Researchers texts cite

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Bi-partite networks Researchers texts cite

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One-mode projection What mode given priority? (Duality of social actors and social groups; e.g. Breiger 1974; Breiger and Pattison, 1986) Loss of information

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**ERGM for bipartite networks**

The model is the same as for one-mode networks (Wang et al., 2007) i j i j i j i j k ℓ k ℓ k ℓ k ℓ Edges ”people” 2-stars ”affiliation” 2-stars 3-paths 4-cycles

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**ERGM for bipartite networks**

j A form of bipartite clustering Also known as Bi-cliques One possible interpretation: k ℓ 4-cycles

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**ERGM for bipartite networks**

j A form of bipartite clustering Also known as Bi-cliques One possible interpretation: k ℓ Who to appoint? 4-cycles x How about x?

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**ERGM for bipartite networks**

Fitting the model in (B)Pnet straightforward extension These statistics are all Markov: i j i j i j i j k ℓ k ℓ k ℓ k ℓ Edges ”people” 2-stars ”affiliation” 2-stars 3-paths 4-cycles <BPNet>

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Part 7 Missing data

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**Methods for missing network data**

Effects of missingness Perils Some investigations on the effects on indices of structural properties (Kossinets, 2006; Costenbader & Valente, 2003; Huisman, 2007) Problems with the “boundary specification issue” Few remedies Deterministically “complement” data (Stork & Richards, 1992) Stochastically Impute missing data (Huisman, 2007) Ad-hoc “likelihood” (score) for missing ties (Liben-Nowell and Kleinberg, 2007)

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**If you don’t have a model for what you have observed **

Model assisted treatment of missing network data If you don’t have a model for what you have observed How are you going to be able to say something about what you have not observed using what you have observed missing data observed data

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**Bayesian data augmentation (Koskinen, Robins & Pattison, 2010)**

Model assisted treatment of missing network data Importance sampling (Handcock & Gile 2010; Koskinen, Robins & Pattison, 2010) Stochastic approximation and the missing data principle (Orchard & Woodbury,1972) (Koskinen & Snijders, forthcoming) Bayesian data augmentation (Koskinen, Robins & Pattison, 2010)

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**We should include counts of:**

Marginalisation (Snijders, 2010; Koskinen et al, 2010) Subgraph of ERGM not ERGM Dependence in ERGM But if k ? j We may also have dependence k k l i i j j We should include counts of:

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**Bayesian Data Augmentation Bayesian Data Augmentation**

Simulate parameters With missing data: In each iteration simulate graphs missing

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**Most likely missing given current **

Bayesian Data Augmentation Bayesian Data Augmentation Simulate parameters With missing data: Most likely missing given current missing In each iteration simulate graphs

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**Most likely given current missing**

Bayesian Data Augmentation Bayesian Data Augmentation Simulate parameters With missing data: Most likely given current missing missing In each iteration simulate graphs

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**Most likely missing given current **

Bayesian Data Augmentation Bayesian Data Augmentation Simulate parameters With missing data: Most likely missing given current missing In each iteration simulate graphs

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**Most likely given current missing**

Bayesian Data Augmentation Bayesian Data Augmentation Simulate parameters With missing data: Most likely given current missing missing In each iteration simulate graphs

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**Most likely missing given current **

Bayesian Data Augmentation Bayesian Data Augmentation Simulate parameters With missing data: Most likely missing given current missing In each iteration simulate graphs

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**and so on… Bayesian Data Augmentation Bayesian Data Augmentation**

Simulate parameters With missing data: and so on… missing In each iteration simulate graphs

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**… until Bayesian Data Augmentation Bayesian Data Augmentation**

Simulate parameters With missing data: … until missing In each iteration simulate graphs

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**Bayesian Data Augmentation Bayesian Data Augmentation**

What does it give us? Distribution of parameters Distribution of missing data Subtle point Missing data does not depend on the parameters (we don’t have to choose parameters to simulate missing) missing

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**Boston office: Hartford office: Providence off.:**

Lazega’s (2001) Lawyers Bayesian Data Augmentation Collaboration network among 36 lawyers in a New England law firm (Lazega, 2001) Boston office: Hartford office: Providence off.: least senior: most senior:

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**Bayesian Data Augmentation**

Lazega’s (2001) Lawyers Bayesian Data Augmentation Edges: (bi = 1, if i corporate, 0 litigation) Main effect Seniority: t1 : Practice: Homophily Practice: t2 : Sex: Office: t3 : GWESP: with 8 = log() etc.

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**Lazega’s (2001) Lawyers – ERGM posteriors (Koskinen, 2008)**

Bayesian Data Augmentation

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**Remove 200 of the 630 dyads at random **

Cross validation (Koskinen, Robins & Pattison, 2010) Bayesian Data Augmentation Remove 200 of the 630 dyads at random Fit inhomogeneous Bernoulli model obtain the posterior predictive tie-probabilities for the missing tie-variables Fit ERGM and obtain the posterior predictive tie-probabilities for the missing tie-variables (Koskinen et al., in press) Fit Hoff’s (2008) latent variable probit model with linear predictor Tz(xij) + wiwjT Repeat many times

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**ROC curve for predictive probabilities combined over 20 replications (Koskinen et al. 2010)**

Bayesian Data Augmentation

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**ROC curve for predictive probabilities combined over 20 replications (Koskinen et al. 2010)**

Bayesian Data Augmentation

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**ROC curve for predictive probabilities combined over 20 replications (Koskinen et al. 2010)**

Bayesian Data Augmentation

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**... but snowball sampling rarely used when population size is known... **

Bayesian Data Augmentation Snowball sampling design ignorable for ERGM (Thompson and Frank, 2000, Handcock & Gile 2010; Koskinen, Robins & Pattison, 2010) ... but snowball sampling rarely used when population size is known... Using the Sageman (2004) “clandestine” network as test-bed for unknown N

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Part 7 Spatial embedding

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**306 actors in Victoria, Australia**

Spatial embedding – Daraganova et al SOCNET 306 actors in Victoria, Australia

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**306 actors in Victoria, Australia**

Spatial embedding – Daraganova et al SOCNET 306 actors in Victoria, Australia spatially embedded ... all living within 14 kilometres of each other

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**306 actors in Victoria, Australia**

Spatial embedding – Daraganova et al SOCNET 306 actors in Victoria, Australia Bernoulli conditional on distance Empirical probability ... all living within 14 kilometres of each other

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**ERGM: distance and endogenous dependence explain different things**

Spatial embedding – Daraganova et al SOCNET Edges -4.87* (0.13) 1.56* (0.65) -4.79* (0.66) -0.20 (0.87) Alt. star -0.86* (0.18) -0.86* (0.2) Alt. triangel 2.74* (0.15) 2.69* (0.14) Log distance -0.78* (0.08) -0.56* (0.07) Age heterophily -0.07* (0.01) 0.001 (0.07) 0.002 (0.06) Gender homophily -1.13* (0.61) -1.13 (0.69) 0.09 (0.83) 0.07 (0.47) ERGM: distance and endogenous dependence explain different things

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Part 8 Further issues

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**modelling actor autoregressive attributes**

Other extensions There are ERGMs (ERGM-like modesl) for directed data valued data bipartite data multiplex data longitudinal data modelling actor autoregressive attributes

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**ERGMs typically assume homogeneity**

Further issues – relaxing homogeneity assumption ERGMs typically assume homogeneity Block modelling and ERGM (Koskinen, 2009) Latent class ERGM (Schweingberger & Handcock)

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**Bayes factors (Wang & Handcock…?)**

Further issues – model selection Assessing Goodness of Fit: Posterior predictive distributions (Koskinen, 2008; Koskinen, Robins & Pattison, 2010; Caimo & Friel, 2011) Non-Bayesian heuristic GOF (Robins et al., 2007; Hunter et al., 2008; Robins et al., 2009; Wang et al., 2009) Model selection Path sampling for AIC (Hunter & Handcock, 2006); Conceptual caveat: model complexity when variables dependent? Bayes factors (Wang & Handcock…?)

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**Bayes is the way of the future Legitimacy and dissemination **

Wrap-up ERGMs Increasingly being used Increasingly being understood Increasingly being able to handle imperfect data (also missing link prediction) Methods Plenty of open issues Bayes is the way of the future Legitimacy and dissemination - e.g. Lusher, Koskinen, Robins ERGMs for SN, CUP, 2011

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**Remaining question: used to be p*…**

... why ERGM?

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