Matlab graph connectivity

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You are now following this question You will see updates in your activity feed. You may receive emails, depending on your notification preferences. John Doe on 26 Apr Vote 0. Accepted Answer: Kelly Kearney. I want to graph the structure of a network a power grid. I have a list containing to-from nodes for each branch. The drawback is that the lines always go out the "bottom" of the ancestor block, and into the "top" of the descendant.

As an ancestor is always displayed above its descendants, the graphs are sometimes very chaotic. It would be much better if the graph was plotted in a way that just showed which nodes were connected, without any hierarchy, and where lines could be horizontal. Is this possible? It doesn't need to be a perfect solution, any improvements would be great.

This is the code I use now:. Nodes newFaultNodes'ID'. Nodes fliplr newFaultNodes'ID'.

Importing and plotting experimental data in matlab

Accepted Answer. Kelly Kearney on 29 Apr Vote 1. Cancel Copy to Clipboard.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time.

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Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have a list containing to-from nodes for each branch. I don't have coordinates for the nodes, and the system topology changes for every simulation. The drawback is that the lines always go out the "bottom" of the ancestor block, and into the "top" of the descendant.

matlab graph connectivity

As an ancestor is always displayed above its descendants, the graphs are sometimes very chaotic for large systems. I have tried changing the property 'LayoutType' of the biograph from the default 'hierarchical' to both 'radial' and 'equilibrium', but this gives even worse results.

Is what I'm asking possible? It doesn't need to be a perfect solution, any improvements would be great. Try out matlab-bgl. It links to the Boost Graph library and includes a few useful layout algorithms. You can then use gplot to visualize.

How to graph a connectivity/adjacency matrix?

Learn more. Ask Question. Asked 6 years, 11 months ago. Active 5 years, 8 months ago. Viewed 4k times. Stewie Griffin. Stewie Griffin Stewie Griffin It looks like grphvis4matlab could work well.

The problem is I can't get it to work. I have installed graphviz The Matlab functions works, but they can't find the graphviz-programs. I get messages like: 'neato' is not recognized as an internal or external command, operable program or batch file. Please install or upgrade graphViz.Documentation Help Center.

After you create a graph object, you can learn more about the graph by using object functions to perform queries against the object. For example, you can add or remove nodes or edges, determine the shortest path between two nodes, or locate a specific node or edge.

The location of each nonzero entry in A specifies an edge for the graph, and the weight of the edge is equal to the value of the entry. The number of elements in nodenames must be equal to size A,1.

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The table must have the same number of rows as A. Specify node names using the table variable Name. You must specify A and optionally can specify nodenames or NodeTable. To use only the upper or lower triangle of A to construct the graph, type can be either 'upper' or 'lower'. You can use any of the input argument combinations in previous syntaxes.

Specify node names using the Name table variable. The EdgeTable input must be a table with a row for each corresponding pair of elements in s and t. Specify edge weights using the table variable Weight. With this syntax, the first variable in EdgeTable must be named EndNodesand it must be a two-column array defining the edge list of the graph. That is, any k that satisfies EdgeTable. EndNodes k,2 is ignored. You must specify EdgeTable and optionally can specify NodeTable.

Adjacency matrix, specified as a full or sparse, numeric matrix. The entries in A specify the network of connections edges between the nodes of the graph. The location of each nonzero entry in A specifies an edge between two nodes. The value of that entry provides the edge weight. A logical adjacency matrix results in an unweighted graph.

Nonzero entries on the main diagonal of A specify self-loopsor nodes that are connected to themselves with an edge. Use the 'omitselfloops' input option to ignore diagonal entries. A must be symmetric unless the type input is specified. Use issymmetric to confirm matrix symmetry. For triangular adjacency matrices, specify type to use only the upper or lower triangle.

The edge between node 1 and node 2 has a weight of 1and the edge between node 1 and node 3 has a weight of 5. Data Types: single double logical. Node names, specified as a cell array of character vectors or string array. Data Types: cell string. Type of adjacency matrix, specified as either 'upper' or 'lower'.

Node pairs, specified as node indices or node names. In all cases, s and t must have the same number of elements. If s and t are numeric, then they correspond to indices of graph nodes. Numeric node indices must be positive integers greater than or equal to 1. If s and t are character vectors, cell arrays of character vectors, or string arrays, then they specify names for the nodes.

The Nodes property of the graph is a table containing a Name variable with the node names, G.Documentation Help Center. For example, plot G,'-or' uses red circles for the nodes and red lines for the edges. The option, axcan precede any of the input argument combinations in previous syntaxes.

Use this object to inspect and adjust the properties of the plotted graph. Create and plot a graph. Create a directed graph, and then plot the graph using the 'force' layout. Plot the graph using custom coordinates for the nodes. The x-coordinates are specified using XDatathe y-coordinates are specified using YDataand the z-coordinates are specified using ZData. Use EdgeLabel to label the edges using the edge weights. Plot the graph, labeling the edges with their weights, and making the width of the edges proportional to their weights.

Use a rescaled version of the edge weights to determine the width of each edge, such that the widest line has a width of 5.

matlab graph connectivity

Create a directed graph. Plot the graph with custom labels for the nodes and edges. Create and plot a directed graph. Specify an output argument to plot to return a handle to the GraphPlot object. Change the x and y coordinates of the nodes. Input graph, specified as either a graph or digraph object.

Use graph to create an undirected graph or digraph to create a directed graph. Line style, marker symbol, and color, specified as a character vector or string vector of symbols. The symbols can appear in any order, and you can omit one or more of the characteristics. If you omit the line style, then the plot shows solid lines for the graph edges.

Example: '--or' uses red circle node markers and red dashed lines as edges. Axes object. If you do not specify an axes object, then plot uses the current axes gca. Specify optional comma-separated pairs of Name,Value arguments.

Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1, The graph properties listed here are only a subset. For a complete list, see GraphPlot Properties.Documentation Help Center. Graphs model the connections in a network and are widely applicable to a variety of physical, biological, and information systems.

You can use graphs to model the neurons in a brain, the flight patterns of an airline, and much more. Each node represents an entity, and each edge represents a connection between two nodes. For more information, see Directed and Undirected Graphs. Directed and Undirected Graphs. Graphs and Matrices.

This example shows an application of sparse matrices and explains the relationship between graphs and matrices. Modify Nodes and Edges of Existing Graph. This example shows how to add attributes to the nodes and edges in graphs created using graph and digraph. Graph Plotting and Customization. This example shows how to plot graphs, and then customize the display to add labels or highlighting to the graph nodes and edges. Label Graph Nodes and Edges. This example shows how to customize the GraphPlot data cursor to display extra node properties of a graph.

This example shows how to define a function that visualizes the results of bfsearch and dfsearch by highlighting the nodes and edges of a graph. Graph and Digraph Classes. Construct and analyze a Watts-Strogatz small-world graph.

matlab graph connectivity

The Watts-Strogatz model is a random graph that has small-world network properties, such as clustering and short average path length. Use a PageRank algorithm to rank a collection of websites. Although the PageRank algorithm was originally designed to rank search engine results, it also can be more broadly applied to the nodes in many different types of graphs. The PageRank score gives an idea of the relative importance of each graph node based on how it is connected to the other nodes.

Use the Laplacian matrix of a graph to compute the Fiedler vector. The Fiedler vector can be used to partition the graph into two subgraphs.Documentation Help Center.

Display the image, noting that the largest component happens to be the two consecutive f's in the word different. Input binary image, specified as a numeric or logical array of any dimension. For numeric input, any nonzero pixels are considered to be on. Data Types: single double int8 int16 int32 int64 uint8 uint16 uint32 uint64 logical.

Pixel connectivity, specified as one of the values in this table. The default connectivity is 8 for 2-D images, and 26 for 3-D images. Pixels are connected if their edges touch. Two adjoining pixels are part of the same object if they are both on and are connected along the horizontal or vertical direction.

Pixels are connected if their edges or corners touch. Two adjoining pixels are part of the same object if they are both on and are connected along the horizontal, vertical, or diagonal direction. Pixels are connected if their faces touch.

Two adjoining pixels are part of the same object if they are both on and are connected in:. Pixels are connected if their faces or edges touch. Two adjoining pixels are part of the same object if they are both on and are connected in. Pixels are connected if their faces, edges, or corners touch. A combination of three directions, such as in-right-up or in-left-down.

For higher dimensions, bwconncomp uses the default value conndef ndims BW ,'maximal'. Connectivity can also be defined in a more general way for any dimension by specifying a 3-byby The 1 -valued elements define neighborhood locations relative to the center element of conn.

Note that conn must be symmetric about its center element. See Specifying Custom Connectivities for more information. Data Types: double logical. The functions bwlabelbwlabelnand bwconncomp all compute connected components for binary images.

It uses significantly less memory and is sometimes faster than the other functions. To extract features from a binary image using regionprops with default connectivity, just pass BW directly into regionprops i.Brain Connectivity Toolbox. Search this site. Getting started. Latest releases. All functions. All help headers.

Graph and Network Algorithms

Network construction. List of measures. Network models. Network Based Statistic Toolbox. Network visualization. Datasets and demos. Download the Toolbox. The Brain Connectivity Toolbox brain-connectivity-toolbox. Reference and citation Complex network measures of brain connectivity: Uses and interpretations.

Brain Connectivity Toolbox in other projects. The Brain Connectivity Toolbox codebase is widely used by brain-imaging researchers, and has been ported to, included in, or modified in, the following projects: bctpy : Brain Connectivity Toolbox for Python. Virtual Brain Project : A consortium for simulation of primate brain-network dynamics. FastFC : Efficient computation of functional brain networks. Brain Connectivity Toolbox in other projects The Brain Connectivity Toolbox codebase is widely used by brain-imaging researchers, and has been ported to, included in, or modified in, the following projects: bctpy : Brain Connectivity Toolbox for Python.

matlab graph connectivity

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