Graphlet Graph Script File Download

To nest graphlets, copy them starting from the lowest level into your clipboard using SAVE SCRIPT > TO CLIPBOARD, then paste into the next highest-level graph, until you have the final graph. Then, save the final graph using SAVE SCRIPT > TO DATA TABLE. Remember to then save the table. When you open the table, you can run the script from the Tables Panel, see the highest-level graph and drill sequentially into the lower-level graphs.

I see there is an auto-generated script "Fit Selected Items" in the pValues data table, but I can't seem to figure out how to modify the JSL to paste into the graphlet editor and have those plots come up as a graphlet. Any help would be appreciated (also might be a very nice default setting!)

Download File 🔥 https://shoxet.com/2y4CBL 🔥

Thanks for the nice use case. You were on the right track by using an existing script of the graph you wanted to see as the starting point for your graphlet. The trick to get a dynamic graphlet - one that reflects the data in the visual element inspected with the mouse - is to use the the information that is available in the Hover Label Execution Context. I like to use the "Context" Textlet preset to get started. The image below shows where it is in the Hover Label Editor:

I am interested in to get this information also be utilized in textlet or graphet script. How do I get hovered rows to list? I can use following syntax for selected rows but well, hovering does not make selection. And as far as I know, there is not "Get hovered rows" function. Any suggestions?

Research of homogeneous (single node and edge type) biological networks (BNs) has receivedsignificant attention. Graphlets have been proven in homogeneous BN research. Given the availability ofBNs of different types (Figure 1), we generalize homogeneous graphlets to their heterogeneous counterparts, whichencompass different node or edge types (Figure 2) and thus allow for analyzing a heterogeneous "network of networks" (Figure 1).We illustrate the usefulness of heterogeneous graphlets in the context of network alignment (NA). Whileexisting NA methods are homogeneous (they account for a single node and edge type), we generalize threestate-of-the-art homogeneous graphlet-based NA methods, WAVE, MAGNA++, and SANA, into their heterogeneous counterparts. Because graphlets are applicable to many network science problems,we provide an intuitive implementation (graphical user interface - GUI) and source code of our heterogeneous graphlet counting approach. Also, we provide code for our heterogeneous versions of the three NA methods. For detailed description and usage instructions, see below.

Example input and output files can be downloaded here. The input should be the network of interest and colors of nodes/edges in the network, and the output will be a file containing, for each node, the given node's node/edge-colored graphlet degree vector (GDV), which counts how many times the given node touches each of the node/edge-colored graphlets at each of the automorphism orbits. The notion of the node/edge-colored GDV of a node is an extension of the existing notion of the homogeneous GDV of a node, except that the former accounts for node/edge colors, while the latter does not.

Note: since graphlet counting is typically time-consuming, we suggest it to be done on a Linux server rather than on a personal Windows- or Mac OS X-based computer. This is exactly why we provide a command-line interface for our heterogeneous graphlet counting software.

The network file should be in the standard gw format (that LEDA uses; one can use GraphCrunch's list2leda script to convert from the edge adjacency list format to the gw format). Edge weights (e.g., "0" in "1 2 0 |{1}|" below) or edge names (e.g., "|{1}|" in "1 2 0 |{1}|" below), if any, will be ignored by our counting program. An example file is shown below:

This network has two edge colors (color 1 and color 2), so there will be three possible edge-colored graphlets for each homogeneous graphlet (corresponding to the following three color combinations: {1}, {2}, {1,2}). Note that we produce heterogeneous graphlets for all up to 5-node homogeneous graphlets, where the homogeneous graphlets and their orbits follow the standard nomenclature (see Figure 1 in this paper). Orbits 0-2 of the edge-colored GDV correspond to the first homogeneous graphlet orbit (homogeneous orbit 0 in Figure 1 in this paper, i.e., an edge), orbits 3-5 of the edge-colored GDV correspond to the second homogeneous orbit (homogeneous orbit 1 in Figure 1 in this paper, i.e., the outer nodes of a three node path), and orbits 6-8 correspond to the third homogeneous orbit (homogeneous orbit 2 in Figure 1 in this paper, i.e., the middle of a three node path). The same applies to all other orbits that are not shown. Details on how to map a heterogeneous orbit to a homogenous orbit and to a set of colors can be found in our README.Given all possible 73 orbits for 2-5-node homogeneous graphlets, (numbered from 0 to 72 in Figure 1 in this paper), and given k node/edge colors, there will be 73(2k-1) orbits in the node/edge-colored GDV.

Code for homogeneous WAVE can be found here. See the README included in the .zip for usage details on how to combine this code with heterogeneous graphlet-based node similarity information in order to run our heterogeneous version of WAVE.

Probabilistic graphical models (@Lauritzen:1996) have become animportant scientific tool for finding and describing patterns inhigh-dimensional data. Learning a graphical model from data requires asimultaneous estimation of the graph and of the probability distributionthat factorizes according to this graph. In the Gaussian case, theunderlying graph is determined by the non-zero entries of the precisionmatrix (the inverse of the population covariance matrix). Gaussiangraphical models have become popular after the advent of computationallytractable estimators, such as neighborhood selection (@Meinshausen:2010)and sparse inverse covariance estimation (@Banerjee2008, @Yuan:2007). State-of-the-art solvers arethe Graphical Lasso (GLASSO) (@Friedman2008) and the QUadraticapproximation for sparse Inverse Covariance estimation (QUIC) method(@Hsieh2014).

Two major shortcomings in StARS are computational cost and optimalsetting of beta. StARS must repeatedly solve costly global optimizationproblems (neighborhood or sparse inverse covariance selection) over theentire regularization path for N sets of subsamples (where the choice ofN is user-defined). Also, there may be no universally optimal setting of as edge stability is strongly influenced by the underlying unknowntopology of the graph (@Ravikumar2011b).

We alleviated both of these shortcomings (see @Mueller:2016). Firstly, we speed upStARS by proposing -dependent lower and upper bounds on theregularization path from as few as N = 2 subsamples (Bounded StARS(B-StARS)). This implies that the lower part of regularization path(resulting in dense graphs and hence computationally expensiveoptimization) does not need to be explored for future samples withoutcompromising selection quality. Secondly, we generalized the concept ofedge stability to induced subgraph (graphlet) stability. We use thegraphlet correlation distance (gcd) (@Yaveroglu2014) as a novel variabilitymeasure for small induced subgraphs across graph estimates. Requiringsimultaneously edge and graphlet stability (gcd+StARS or G-StARS) leadsto superior regularization parameter selection on synthetic benchmarksand real-world data.

The option to find lower/upper bounds on the StARS-selected lambdafrom N=2 subsamples works particularly well when the underlying targetgraphs are sparse or when the dimensionality is high (above 20 variablesor so). The bounds greatly reduce computational burden even when runningin embarrassingly parallel (batch) mode.

You can pass in an arbitrary graphical model estimation function tofun. The function has some requirements: the first argumentmust be the nxp data matrix, and one argument must be namedlambda, which should be a decreasing numeric vectorcontaining the lambda path. The output should be a list of adjacencymatrices (which can be of sparse representation from theMatrix package to save memory). Here is an example fromQUIC.

We can use the graphlet correlation distance (gcd) as an additionalstability criterion (G-StARS). We could call pulsar againwith a new criterion, or simply update the arguments formodel we already used. Then, we can use our default approach forselecting the optimal index, based on the gcd+StARS criterion: choosethe minimum gcd summary statistic between the upper and lower StARSbounds.

For large graphs, we could reduce pulsar run time byrunning each subsampled dataset in parallel (i.e., each run as anindependent job). This is a natural choice since we want to infer anindependent graphical model for each subsampled dataset.

We also potentially gain efficiency in memory usage. Even for memoryefficient representations of sparse graphs, for a lambda path of size Land for number N subsamples we must hold L*Np*p- sized adjacency matrices in memory to compute thesummary statistic. batchtools lets us use a MapReduce strategy, so thatonly one p*p graph and one p*p aggregationmatrix needs to be held in memory at any time. For large p, it can bemore efficient to read data off the disk.

It is also possible to run B-StARS in batch. The first two jobs(representing the first 2 subsamples) will run to completion before thefinal N-2 are run. This serializes the batch mode a bit but is, ingeneral, faster whenever individual jobs require costly computationoutside the lambda bounds, e.g., when some of the provided lambda valuesalong the regularization path induce dense graph estimates. e24fc04721