I have two affiliation matrices - one with organizations and networks (orgs as rows and networks as columns) and a second with organizations acknowledged in published reports (orgs as the rows, publications as the columns) and there is overlap at the organization level. I am trying to figure out how to graph this on one graph rather than two which is how I currently have it. Any suggestions would be much appreciated!
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I need to visualize a very large number (10k) of distinct networks, all on the same page, and label each node a type (binary). Each network aside from a few are relatively small. Link lengths/weighting is not important for this dataset, and a high degree of overlap on the bigger networks is fine as long as density is evident.
I have gone over some of the Igraph documentation and have been able to create individual graphs using a node/link list. However, I would like some insight if possible on how to translate this large number of networks (currently just 10k arrays with node identity and type inside) into 10k plots, and whether this is a feasible task with Igraph.
Any insight is greatly appreciated.
Suppose I have large network dataset which contains edge-list . I'd like to splits (homogeneously or hetrogeneously) network structure into two or more than communities in such a way a way that every vertex belongs to one of the communities.
this image have three small subnetworks or communities are connected to each other
I hope this has not been asked before, but I am currently in the process of analyzing some microscopy pictures in R and I am not quite sure how to tackle this.
The situation is as follows:
- I have several pictures of different targets in cells which show spots of signal
- Some pictures show the same cells, but were aquired after others and are therefore a little "off" in x-, y- and z-direction
- Some, but by no means all of the pictures show colocalization = spots from one picture also show up on other pictures
Coming from the spot detection software, I now have data frames for all spots in each picture (one df per picture) with the x-, y- and z-coordinates.
I am now looking for
a) a way to align these matrices of spots from the different colors and thought that cross-correlation of the matrices might be a way to go (however, is there CC in 3D in R?)
b) a way to calculate the colocalization. As these are pictures and intrinsically noisy, even colocalized spots might have a little different coordinates. Is there a function of package in R which merges these data based on a threshold or other parameter of my choice?
Thanks a lot in advance for all your answers!!
Simon
I'm currently doing a Network Analysis in R, using the Statnet package (http://statnet.csde.washington.edu/)
So far I have been able to:
1) Construct Social Networks using the network() function. The information I had was originally contained in a incidence matrix form (one row per project, project members in the columns), which I transformed to adjency (socio-matrix) form before building the networks in R.
2) Plot the networks and compute general statistics (both for the overall network and for network vertexes)
3) Add external vertex attributes to the networks
4) Plot the networks with vertexes in different colors according to their attributes.
Right now I would like to complement my analysis by adding edge attributes to the network (to differentiate relationships, for example different kinds of projects).
I know I can use the set.edge.attribute() function, and that I need to input a vector of the same size as the total of edges that exist in the network. However, I don't know exactly how to sort this vector of edge attributes, in order for the function to assign them correctly to the corresponding edges.
Anybody knows how the network() function sorts edges internally? Any ideas on how to sort my vector of edge attributes in order to get a appropriate matching?
Thanks in advance for your help!
The network() function can create networks from various types of inputs (such as an edgelist or an adjacency matrix) so it would help if you gave a bit more of a code example explaining how you are creating the network and the format of the structure your edge attribute data are in.
I believe that in general network() will create the edges in the same order as the input edgelist. So if your edge attribute vector is in the same order as your original data, you should be able to use set.edge.attribute() safely. If your edge attributes are in a matrix form, you can use set.edge.value().
I work for game development company which makes casual games. One of the main casual genres is match-3: there is a field and chips of different colors. One should move chips so that they make lines of at least three chips of the same color. If the move leads to making a line the chips in the line disappear.
Chips on field can be located differently: there may be a lot of chips of the same color gouped in one place or there may be a situation when a player can't make a move - all the neighbour chips are of the different colors.
So, I want to express the situation on the field mathematically with a factor of order (disorder). If the factor is high a player can make a lot of matches and the lines made by the player are long. If the factor is low, the field is in complete disorder and one can't make a single match. This may be helpful for generating field of different difficulty.
The question is: what branch of math can help me to do this. Where should I start my research. Any suggestions for keywords to google?
Thanks in advace.
Entropy.
I would look into graph theory. You can for example make a graph, where nodes would be positions on the board, and two nodes would be connected with an edge if they are neighbours and have a chip of the same color. If you have large components with nodes of large degree, you have less disorder. If all your components are small, you have high disorder.
First thing that comes to mind is that you're looking at the distribution of n populations (one for each color), which I would approach with Poisson sampling,. You can use that to calculate the probability of finding two adjacent units of the same population (color), which will give you a measure of the difficulty of your puzzle.