# A Survey of Statistical Network Models

Presented by Wutao Wei

# Dynamic Network Models

• Birth and death of nodes and edges

# ERGM dynamic version

• Assumption: No edge will be removed after added
• Generate an edge to the network with Probability $$p=E/{n \choose 2}$$
• Degree Distribution: Binomial, and near Poisson when N is large

# Preferential Attachment Model

• One of major criticism to ERGM: not scale-free, not following power law
• PA Model step:
• Time 0: at time 0, there are $$N_0$$ unconnected nodes.
• subsequent time step: a new node added with $$m\leq N_0$$
• connect with m nodes with prob of $$p_i = \frac{\theta{i}}{\sum_{j}\theta{j}}$$
• hubs, Rich-get-richer

# Degree Distribution

• Power Law: $$P(k)\sim k^{-\gamma}$$
• $$P(k)$$ is the fraction of $$\frac{\text{Node with k links}}{\text{Total Nodes}}$$
• ERGM: Poisson
• Dorogovtsev and Mendes: additional decaying as $$(t-t_i)^{\nu}$$, $$t_i$$ is the age of $$k_i$$

# Small-World Models

• Watts-Strogatz: begin with a ring lattice with N nodes and k edges per node
• Randomly rewires each edge with probability p
• As p goes from 0 to 1, the construction moves toward an ERGM
• Model is not dynamic

# Kleinberg Model

• random edges are added to a fixed grid
• probability of connection depends on the distance in the grid
• $$P(\text{x,y are connected})\sim d(x,y)^{-\alpha}$$
• Extension: Clauset and Moore, limited the steps of rewiring
• converges to a power law, where $$\alpha=\alpha_{rewired}$$

# Sandberg and Clark Model

• Different rewireing scheme
• at each time step $$j=1,2,3,\dots$$, choose a random start node x and a target node y
• perform greedy routing from x to y, denoting the routing is $$\{x,z_0,z_1,\dots,z_n,y\}$$
• independently and with (small) probability p, update the long-range link of each node $$z_i$$ on the resulting path to $$z_i$$ to y
• When stationary, the distribution of distances spanned by long-range links is theoretical optimum for search and the expected length of searches is polylogaithmic
• Example: P2P networks

# Remark of 3.7 Fix Degree Random Graph Models

• full symmetric and the expected degree is the same for all nodes
• fix the degree-distribution parameters or distribution on some statistics
• Relationship with $$p_1$$ model:
• when $$\rho = 0$$, look into distribution on minimum sufficient statistics
• calculate $$\{\alpha_i\}$$ and $$\{\beta_j\}$$ by using in-degrees and out-degrees

# Duplication-Attachment Models

• Denote the graph at time t as $$G_t=(\mathcal{N}_t,\mathcal{E}_t)$$
• when t+1, add a node N into $$G_t$$, and it is connected to a prototype node $$m$$, chosen uniformly in $$\mathcal{N}_t$$
• Then $$d$$ out-links are added to node N.
• The ith out-link is chosen with probability $$\alpha$$, it can connect to any nodes in $$\mathcal{N}_t$$ uniformly
• $$1-\alpha$$, it connects back to m
• extension: relate to distance with $$p^{-d(v,w)/2}$$; mixture models

# Continuous Time Markov Chain Models

• Continuous Markov Chain Process(CMCP) + ERGM
• Markov condition: for any possible outcome $$\tilde{y}\in\mathcal{Y}$$ and any pair of time points $$\{t_a < t_b \mid t_a,t_b\in \mathcal{T}\}$$
• $$Pr\{Y(t_b)=\tilde{y} \mid Y(t)=y(t), \forall t: t\leq t_a\}=Pr\{Y(t_b)=\tilde{y} \mid Y(t_a)=y(t_a)$$
• CMCP: $$Pr(t)=e^{tQ}$$, where $$Q$$ is the intensity matrix with elements $$q(y,\tilde{y})$$

# Independent Arc, Reciprocity, and Popularity Models

• Independent arc model: $$q_{ij}(\mathbf y)=\lambda_{y_{ij}}$$
• rate from 0 to 1 is $$\lambda_0$$, form 0 to 1 is $$\lambda_1$$; not depending on other edges
• Reciprocity model: $$q_{ij}(\mathbf y)=\lambda_{y_{ij}} + \mu_{y_{ij}} y_{ji}$$
• Popularity model: $$q_{ij}(\mathbf y)=\lambda_{y_{ij}} + \pi_{y{ij}} y_{+j}$$
• Expansiveness model: $$q_{ij}(\mathbf y)=\lambda_{y_{ij}} + \pi_{y{ij}} y_{i+}$$

# Edge-Oriented Dynamics

• two components: opportunity for change and propensity of change
• or to say one control when it happens and the other controls probability of generation of edges
• general form: $$q_{ij}(\mathbf y)=\rho p_{ij}(\mathbf y)$$
• $$p_{ij}(\mathbf y)=\frac{exp(f(y(i,j,1-y{ij})))}{exp(f(y(i,j,0)))+exp(f(y(i,j,1)))}$$
• $$f(\mathbf y)=\sum_k \beta_k s_k(\mathbf y)$$
• degeneracy

# Node-Oriented Dynamics

• general form: $$q_{ij}(\mathbf y)=\rho p_{i}(\mathbf y)$$
• $$p_{ij}(\mathbf y)=\frac{exp(f(y(i,j,1-y{ij})))}{\sum_{h\neq i}exp(f(y(i,h,1-y_{ih})))}$$
• $$f(\mathbf y)=\sum_k \beta_k s_k(\mathbf y)$$
• also can compose edge-node mixed dynamics: $$q_{ij}(\mathbf y)=\rho \frac{exp(f(y(i,j,1-y{ij})))}{\sum_{h\neq i}exp(f(y(i,h,1-y_{ih})))}$$
• Remark: estimation in CPCM by using method of moments via MCMC

# Discrete Time Markov Models

• satisfy Markov property
• $$Pr(Y^1,Y^2,\dots,Y^T)=Pr(Y^T \mid Y^{T-1})Pr(Y^{T-1} \mid Y^{T-2})\cdots Pr(Y^2 \mid Y^{1})$$
• $$\{Y^1,Y^2,\dots,Y^T\}$$ is a sequence of snapshots of network

# Discrete Markov ERGM Model

• $$Pr(\mathbf y^t \mid \mathbf y^{t-1})=\frac{1}{Z} \exp\{\sum_k \beta_k s_k(\mathbf y^t, \mathbf y^{t-1})\}$$
• Density of edges: $$s_1(\mathbf y^t, \mathbf y^{t-1})=\frac{1}{n-1}\sum_{ij} y_{ij}^t$$
• Stability: $$s_2(\mathbf y^t, \mathbf y^{t-1})=\frac{1}{n-1}\sum_{ij} [y_{ij}^t y_{ij}^{t-1} + (1-y_{ij}^t)(1-y_{ij}^{t-1})]$$
• Reciprocity: $$s_3(\mathbf y^t, \mathbf y^{t-1})= n \dfrac{\sum_{ij} y_{ji}^t y_{ij}^{t-1}}{\sum_{ij} y_{ij}^{t-1}}$$
• Transitivity: $$s_4(\mathbf y^t, \mathbf y^{t-1})= n \dfrac{\sum_{ijk} y_{ik}^t y_{ij}^{t-1} y_{jk}^{t-1}}{\sum_{ij} y_{ij}^{t-1} y_{jk}^{t-1}}$$
• Markov property: $$Pr(Y^{K+1},Y^{K+2},\dots, Y^T \mid Y^1,\dots,Y^K)=\prod_{t=K+1}^T Pr(Y^T \mid Y^{t-K},\dots,Y^{t-1})$$
• where $$Pr(Y^t \mid Y^{t-K},\dots,Y^{t-1})=\frac{1}{Z}\exp\{\sum_k \beta_k s_k (Y^t,\dots,Y^{t-K})\}$$

# Remark 3.6 Inference of Parameters

• likelihood: $$Pr(\mathbf y \mid \mathbf \theta)=\frac{\prod_{c\in\mathcal C} \phi(\mathbf y_c \mid \mathbf \theta_c)}{z}$$
• Write it as exponential family: $$Pr(\mathbf y \mid \mathbf \theta)=\exp\{\mathbf \theta^T u(\mathbf y)-\log z\}$$

# Dynamic Latent Space Model

• allow latent positions to change over time in Gaussian-distributed random steps: $$Z_t\mid Z_{t-1} \sim \mathcal N(Z_{t-1},\sigma^2I)$$
• $$p_{ij}^L:=p^L(y_{ij}=1)=\frac{1}{1+\exp(d_{ij}-r_{ij})}$$
• where $$d_{ij}$$ is the Euclidean distance between i and j,$$r_{ij}$$ is a radius of influence defined as $$c\times (\max(\delta_i,\delta_j)+1)$$
• weigh the link probability with a kernel function $$K(d_{ij})$$, continulus and differentiable at $$r_{ij}$$. $$K(d_{ij})=(1-(d_{ij}/r_{ij})^2)^2$$, when $$d_{ij}<r_{ij}$$, and 0 otherwise.
• we can model link probability $$p_{ij}=p_{ij}^L K(d_{ij})+(1-K(d_{ij}))\rho$$, $$\rho$$ is a noise probability
• Find MLE based on $$Pr(Y^t \mid Z^t)=\prod_{i\sim j}p_{ij}\prod_{i\nsim j}(1-p_{ij})$$

# Remark 3.8 Latent Space Model

• Model:$$\log \frac{P(Y(i,j))}{1-P(Y(i,j))} = \alpha + \beta^{'} X_{ij} - \lvert Z_i - Z_j \lvert \equiv \eta_{ij}$$
• Step 1: Find MLE of $$Z$$ denoted as $$\hat Z$$
• Step 2 a: set $$Z_0=\hat Z$$, also a symmetric proposal distribution $$J(Z\mid Z_k)$$
• b: Sample a $$Z*$$ from $$J(Z\mid Z_k)$$
• c: Accept $$Z*$$ as $$Z_{k+1}$$ with probability $$\frac{p(Y \mid Z*, \alpha_k,\beta_k,X)}{p(Y \mid Z_K, \alpha_k,\beta_k,X)} \frac{\pi{Z*}}{Z_k}$$; or $$Z_{k+1}=Z_k$$
• d: Store $$\tilde Z_{k+1}=\arg\min_{TZ_{k-1}} tr (\hat Z-TZ_{k+1})^'(\hat Z-TZ_{k+1})$$
• Step 3: Update $$\alpha$$ and $$\beta$$ with a Metropolis-Hastings algorithm

# Applications of DLSM

• NIPS paper and physics community co-authorship
• US Supreme Court citation networks in different opinion eras

# Dynamic Contextual Friendship Model(DCFM)

• Context changes, the relationship changes
• weighted network
• Generative process:
• Step 1: For each node i, sample context $$C_i\sim mult(\theta_i)$$, $$\theta_i$$ denotes the context distribution parameters
• Step 2: For each pair of nodes i and j in the same context, sample meeting variable $$M_{ij}\sim Bern(\nu_i\nu_j)$$, where $$\nu_i$$ and $$\nu_j$$ represent the "friendliness" of nodes i and j;
• Step 3: $$$W_{ij}^t=\begin{cases} Poi( \lambda_h (W_{ij}^{t-1}+1)) &amp; \text{if}\ M_{ij}= 1 \\ Poi(\lambda_l(W_{ij}^{t-1})) &amp; \text{otherwise} \end{cases}$$$ where $$\lambda_h$$ and $$\lambda_l$$ are hyperparameters indicating the rates of growth and decay.

# Issues in Network Modeling

• Network Visualization
• Computability
• Asymptotics and Assessing Goodness of Fit
• Sampling
• Missing Data

# Issues in Network Modeling(Continued)

• Predicion
• Embeddability
• Identifiablity
• Combining links with their attributes