Does Highcharts support weighted edges between two nodes? No the global settings for all links but to configure each individual link LENGTH based on weight so that the links indicate closeness between nodes.
For example:
HighCharts network graph
Is HighCharts capable of configuring the lengths of links between 0,1 and 2 based on their corresponding weights?
Related
Is there a way to manually set the minimum and maximum node size and colour ranking based on attributes such as node degree or weighted degree)? In Gephi, when I want to use this ranking, it automatically takes the minimum and maximum of the parameter (which in my case is either degree or weighted degree). I have multiple network files and I want to compare them and every network has a different min and maximum for degree and weighted degree so when I plot them, I cannot compare them because they are plotted based on min and max of every network and only relative to individual network. Is there a way to manually enter an attribute minimum and maximum values for node ranking? I am using Gephi 0.9.2 on Mac.
Hope this helps. This is for In-Degree but it generalises for all attributes
I managed to get it done. What I did is, first import the network file into Gephi and compute weighted degree or Degree statistics. Go to Data tab and look for min and maximum value of these attributes. I check such min and max values in all my networks. then I choose the lowest min value and highest max value out of all my networks. These values you will use in the next step. Next, I import a single network in Gephi, computed Degree and weighted degree statistics. Then went to Data tab and there I added manually two nodes (with any label of your own choice) and then input the min values estimated in the previous step of the required attributes to one node and max values to the second node. When ordering nodes or assigning color or setting size of nodes based on attributes, Gephi then considers those nodes into account and plot the network accordingly. Then I go to filter tab and go to partition tab and select partition by range. There I exclude the nodes based on the attribute values that I manually specified as min and max. Those nodes will disappear from your network and there you have the final network. This way, I was able to compare different networks together visually.
I have a Radial Distribution Graph (having x-axis ie.,[r], y-axis ie., [g(r)] values).
One of the values in the graph has g(r) high value which is around 45 and the rest have around less than 5. Hence it makes the graphs unclear to differentiate.
Since the values are high in this case, I need to normalize it.
How can i normalize the values (the y-axis [g(r)])?
I have attached the graph for visualization.
Any suggestions?
I have numerous return time series spanning over a couple of years. I want to see how stable these series are across time. So far I have winsorized and z-scored my data and created histograms and AVG vs. StdDev graphs. Using the histograms I can see how the distribution looks and check for positive or negative skew, with the Avg vs. StdDev chart I tried to get some kind of density measure within the data set (each data point represents a point in time), i.e a big blob means less stable than a dense one
I am looking for other ways to visualise my data. Any ideas welcome
What is the graph layout algorithm that is used in Neo4j?
I would like to have the paper that explain the graph layout algorithm that is shown in NEO4J.
I wan to know why the nodes are organized in the way Neo4j presents them.
The layout algorithm used for visualizing graphs in the Neo4j browser is a force directed algorithm. From Wikipedia:
Their purpose is to position the nodes of a graph in two-dimensional or three-dimensional space so that all the edges are of more or less equal length and there are as few crossing edges as possible, by assigning forces among the set of edges and the set of nodes, based on their relative positions, and then using these forces either to simulate the motion of the edges and nodes or to minimize their energy.
For academic references, there is a chapter from the Handbook of Graph Drawing and Visualization that covers much of the literature here.
In addition to the Neo4j Browser code linked in stdod--'s answer there is a D3 example of force directed layout here.
To visualize a graph in the neo4j-browser is used d3 library.
View implementation of d3.layout.force here: layout.ts.
I need to find a solution for the following problem. I want to find the centers of circular clusters within a point cloud. For example, in the bottom picture i want to identify 3 centers. I was trying to use clustering algorithms (e.g.,kmeans, kmedians, gaussian mixture models) to find the centers of the clusters, but they did not give proper results without individual parametrization(e.g. number of clusters).
Does anyone has a suggestion which methods can be used for solving this problem?
There are plenty of choices.
Gaussian Mixture Modeling, with a heuristic to optimize the parameters
DBSCAN, with postprocessing to compute cluster centers
OPTICS, with postprocessing to compute cluster centers
Mean-Shift, the most dense points are your centers.
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