Questions tagged [networks]

Refers to network theory as part of the graph theory. For questions about neural networks, use our [neural-networks] tag.

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How to interpret the minimum spanning tree in a fully-connected graph?

Can someone explain how the resulting graph is the minimum spanning tree (MST) from the fully-connected undirected graph? I don't understand how this is interpreted in this context. Definition ...
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Is this Markov chain of a UAU-process (unaware - aware - unaware) convergent and why?

I am currently looking at a Markov chain of $UAU$-process on a uni-weighted undirected network. Where individuals are aware of certain arbitrary information or not. The individuals are the nodes of ...
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How can I model or estimate the probability of a given network connection in a bipartite graph of relationships that may or may not exist?

I have a large set of network data, with many groups. Within each group, there are some criteria for creating "candidate" relationships between entities. Below I have an example of 8 groups, ...
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How can I code, process, and quantify the features of a hierarchical network in a scheme that admits siblingship but also auntship links?

I am new to network analysis and I am currently facing a challenge with coding, processing, and quantifying networks in a hierarchical scheme. In this scheme, nodes pertain to differing hierarchical ...
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How to handle missing data when calculating network homophily/assortativity?

I am trying to calculate network homophily/assortativity for a graph. However, some of my nodes have missing values. It turns out igraph's assortativity functions have no "na.rm" function ...
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Inference on a Gaussian random field / undirected graph?

Assume I have an undirected graph with $D$ nodes, and a $D$-by-$D$ matrix with edge strengths between $0$ (implying conditional indepedence given all other nodes), and $1$ (implying complete ...
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Application of Bayesian Networks to tabular data

I have been going through some tutorials regarding Bayesian Networks, but i have yet to see them applied to tabular data, i.e. a dataset. I have created this dummy example to experiment, and attempt ...
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Compute assortativity for each type of nodes using igraph package

I want to compute the assortativity coefficient for each type of node using the assortativity_nominal() function from igraph package. However, this function only ...
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preferential attachment exponent (pa.exp in R) and network centrality

Is there a paper showing how the preferential attachment exponent (pa.exp in R)of a network affects its centrality? From simulations, I saw that low pa.exp results in low centrality. That is expected. ...
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Graph/Network Clustering Models that Use Covariate Information

I have been reading about Clustering Models on Graph/Network data. For example, there seems to be a popular Clustering Model for Graph/Network data called Louvain Clustering (https://en.wikipedia.org/...
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Normalization of coordinate in panel data? (timeseries)

I'm working with the neural networks and motion prediction on mesh data. Objective of neural network is to predict $T_p$ frames in the future, given previuos $T_s$ frames. We have let's say 10 objects....
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Bootnet case-dropping bootstrap stops with no specific error message

I'm running a network analysis in R using qgraph and bootnet. When running the case-dropping bootstrap to estimate correlation-stability coefficients of centrality indices, the alogithm simply "...
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How are Panel Econometrics and Network Econometrics different?

I am relatively well-versed in the area of panel econometrics, where, say, you have both cross-sectional data and time-series observations, and say, the data is usually denoted by $y_{it}$, where $i=1,...
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Eigenvector centrality comparison

Let's say we calculate the eigenvector centrality of the same set of nodes in different years (we have a network each year). Note that the eigenvector centrality is normalized in such a way that the ...
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Does one network predict the other?

Here is my problem: I have two undirected networks, $G1$ and $G2$ which change over time The nodes in each network are identical The edges are always constrained between 0 and 1 I want to know ...
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How to Cluster Several Graphs?

I'm relatively new to Graph Theory, but I'm wondering if I have a set of Graphs {G1, G2, ..., Gn}, are there any algorithms that allow for clustering these graphs? taking into account the nodes and ...
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How to calculate the number of parameters in a network analysis and what that means for sample size

I'm hoping to conduct a network analysis (Ising model then later add LASSO regularization) on a biobank sample with a lot of data. Something like 2,000+ variables and 90k+ patients. It'd be nice to do ...
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Is in a DAG every node an ancestor or a descendant?

This is the second question that I am asking here about these note about DAGs http://www.stat.cmu.edu/~larry/=sml/DAGs.pdf . When discussing the max-sum algorithm, they want to evaluate the marginal ...
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Weight Adjacency Matrix - Network Analysis

I have a question regarding the output of the Ising Fit Model used for binary data to estimate a network. After estimating the network R gives me an output of the 'weight adjacency matrix'. I am ...
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A network has many conflicting edges. How to create a good visualization for it?

I'm having a network of belief system. It has ~100 nodes, 316 regular edges and 78 conflicting edges. (If belief B conflicts with belief A, then node B and node A share a conflicting edge. If belief B ...
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Looking for an alternative for massive univariate t-tests

For each subject in my dataset, I have a data matrix that contains control energy values for certain state-to-state-transitions. Rows denote the transition that was made, columns denote the nodes in ...
1 vote
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igraph's "assortativity" function returns NaN if all attributes are identical. Why?

I am trying to use igraph's assortativity function. It returns positive values if more similar nodes have similar attributes and negative values otherwise. Indeed, if I randomly generate attributes ...
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2 votes
1 answer
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Normalizing ASPL and CC for Number of Nodes?

I have a bunch of human-built networks of different sizes and am trying to compare them using Small World Network metrics: Average Shortest Path Length (ASPL) and local average Clustering Coefficient (...
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Measuring the divergence in centrality statistics for similar networks?

I want to measure the similarity/divergence between the centrality of nodes in two publicly available word association networks. In my analysis, we have a long list of nodes - 12,000 or so - and then ...
2 votes
2 answers
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What does it mean for Katz Centralities to "diverge"

In Mark Newman's Networks book, 2010 edition, page 173, he explains some mathematical details behind the Katz Centrality measure: In matrix terms, Eq. (7.8) can be written x = αAx + β1, (7.9) where 1 ...
2 votes
1 answer
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Reading on "Economic nowcasting" for a probabilist

I am about to attend an interview for a postdoc in "economic nowcasting". I am work mostly in probability, particularly complex networks and random graphs. I am looking for some good reading,...
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Symbol interpretation in non-constant power law density

I am reading Barabasi book on network science. I am struggling to interpret the density formula for a power-law degree distribution with low-degree saturation and high-degree cut-off (http://...
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How do I know if violations of assumption of sparsity is problematic?

I am running a network analysis in R using the estimateNetwork(default = "EBICglasso") function from the bootnet package. I have set ...
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3 votes
1 answer
527 views

How can this CDF be decreasing? (power-law)

I am reading the first vignette of the powerRlaw R package. It uses the moby dataset, which is an array of word frequency. Each element is a word, and the value if ...
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1 answer
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Network analysis: density of communities/partitions (and other metrics)

After running a community detection algorhythm (e.g. edge betweenness, or greedy modularity), I like know the density of each seperate community, and potentially some other metrics, too. My desired ...
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1 vote
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what would a network looks like if the modularity is almost -1?

I know that larger positive modularity is good, and often lies between -0.5 and 1. But what would the network looks like if the modularity is appoaching -1? For example, equals to -0.7?
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1 vote
1 answer
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Constructing a Weighted Random Graph

I want to create a weighted random graph (in contrast to the unweighted Erdős–Rényi model). I have a list of weights (derived from a real-world network, very skewed distribution that most weights are ...
1 vote
1 answer
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Model Comparison for Weighted Network

I have a weighted network data and I want to know whether this network possesses small-world features or the features of a scale-free network. I know for unweighted networks, I can create comparable ...
1 vote
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Is Kendall's tau adequate to test for a non-monotonic relationship?

Goal Networks can have so many nodes that it's hard to study them. That is why in some cases nodes are clustered together. I am looking at whether the betweenness of a node is maintained after the ...
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1 vote
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How can I perform a meta-analysis on Kendall's tau?

Goal Networks can have so many nodes that it's hard to study them. That is why in some cases nodes are clustered together. I am looking at whether the betweenness of a node similar to the one of its ...
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1 vote
1 answer
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Given a graph, test if some vertices are more connected than the background

I have a neighborhood graph of some 10k vertices (a k-NN graph of single-cell RNA-seq data). I am interested if a given set of vertices is more connected to each other than you would expect by chance. ...
2 votes
2 answers
104 views

Network clustering stability using bootstrapping techniques

I use the standard modularity-maximisation Louvain clustering method to partition a large undirected network into communities. I fear the result of the partition is quite fragile. Is there a standard ...
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1 vote
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Inference in a system in which (almost) all regressors are endogenous

We conducted a number of tests on individual species over a period of several days. Let $x_{k,s,t}$ denote the result of test $k$ for species $s$ at time $t$. In total there are $S$ species, $K$ tests,...
3 votes
1 answer
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Predictions in a system in which (almost) all regressors are endogenous

Consider the following system where all variables are endogenous. \begin{align*} x_{1} & =\beta_{21}x_{2}+\beta_{31}x_{3}+u_{1}\\ x_{2} & =\beta_{12}x_{1}+\beta_{32}x_{3}+u_{2}\\ x_{3} &...
2 votes
1 answer
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social network measure of node's crowdedness

The undirected network below shows the interaction of workers (blue nodes) with products worked on during a time period. The thickness of the edges/arcs indicates how much time the worker spends on a ...
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1 answer
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Evaluation of gene regulatory network

I am new to this forum and data science, I will try my best to describe my question. Question background: I am molecular biology student and have recently inferred a gene expression network using 105 ...
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How to create a Bayesian network? [closed]

I have the following question and I really need help please: Consider the following hypothetical scenario. A car company would like to use a Bayesian Network model to better predict whether a certain ...
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1 answer
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What is the difference between brokerage and betweenness?

This link describes betweenness: "Betweenness centrality measures the extent to which a vertex lies on paths between other vertices." This link describes brokerage: "Brokerage is a ...
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To estimate a Markov random field, do we have to assume a DAG generated the data?

Even if I believe that I understood the Markov Random Fields and DAGs separately, I encountered a question that I have written in the title and cannot come up with a clear-cut answer. Can you help me ...
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1 answer
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Identify individual networks within dataset containing many (using SAS preferably)

I have a dataset of links/edges that looks something like the following (except significantly larger - approx. 50,000 links): ...
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1 answer
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What is the difference between local efficiency and betweenness centrality in network analysis?

There are many network properties that you can extract for a given node. Two I have encountered that sound conceptually very similar to me and that I have difficulty distinguishing are betweenness ...
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Accounting for connectedness during the analysis of nodes in a directed acyclcic graph

I am studying a river network and environmental features measured at different stations along the network. Often, one station is higher up the river than another (or multiple others). I can ...
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4 votes
1 answer
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What are the conditions for a graph's adjacency matrix to not have a negative eigenvalue with magnitude>=1?

Say I have a (directed) graph $G$ with an adjacency matrix $A$. For the sake of the question, let's assume it's normalized column-wise (edge weights are normalized so the sum of out-edge weights per ...
1 vote
0 answers
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Methods to align/reduce distance between two or more 3D graph-spaces?

I'm trying to align multiple graphs of spatial data, similar to image registration, but on a 3D graphspace of a select feature. I have a bunch of point data with x,y,z coordinates that together, ...
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2 answers
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How do I evaluate correlation, that appears non-linear

Directed here from StackOverflow Let's say I want to assess if there is a correlation between two fields, one of which I know to have a power distribution. A lot of the information I read assumes ...
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