I have started to work with gephi to help me display a dataset.
The dataset contains:
tags (terms for a certain picture) as nodes
Normalized Google Similarity Distance between those tags as edges with a weight (between 0 und 1)
Every tag is connected to every other tag, as long as they both belong to the same picture. So I have one cluster of nodes and edges for every picture.
I have now imported this dataset to gephi in the following format:
nodes: id, label
edges: target, source, weight (between 0 and 1)
Like 500 nodes and 6000 edges.
My problem now is that after importing all those nodes and edges the graph looks kind of bunched with no real order. Every cluster of every picture is mixed into other clusters of other pictures.
Now using Modularity as Partition algorithm (which should use the Louvain method) the graph is getting colored, each color represent a picture. Now I can split this mess, using the Force Atlas 2 Layout.
I now have a colored graph with something like 15 clusters (every cluster represent 1 picture)
Now I want to cluster those clusters again using tags (nodes) according to their Normalized google distance (weight of the edges), which should then be tags which are somewhat equal in their meaning.
I hope you guys understand what I want to accomplish.
I can also upload a picture to clarify it.
Thanks a lot
I don't think you can do that with the standard version of Gephi. You would need to develop a plugin to implement the very last step of your process.
Gephi is good for visualizing and browsing graphs, but (for now) there are more complete tools when it comes to processing topological properties. for instance, the igraph library (available in C, R and python) might be more appropriate for you. And note that you can use a file format compatible with both Gephi and igraph, which allows you to use both tools on the same data.
I was able to solve my problem. I had to import every one of these 15 clusters on their own. In this way i could use the Modularity method on just those few.
Related
we know that we can use Dijkstra Algorithm for graph containing multiple sources for example this problem can be solved by using multiple Dijkstra's algorithm for multiple sources. Similarly, can we implement
Bellman-ford algorithm ? If possible share pseudo code.
Assume that the graph contains negative weight edges and no negative cycles.
Sure! One simple way to do this is to turn the original graph, which has multiple sources, into a new graph with exactly one source. Just add in a new node s that has zero-cost directed edges to each of the sources from the original graph. Running Bellman-Ford starting at this new node s then effectively simulates finding shortest paths starting from each of the sources.
I have a graph that consists of nodes. Each node can have multiple parents and/or children. I want to display that graph and connections between nodes.
But I don't know how to redistribute nodes to maximize readability. Currently I'm facing following problems:
Node connections cross each other too much even though it's unnecessary and can be evaded
Connections between nodes are too long visually
Some connections have the same angle so they overlap and become one line
Connections beteween column i and column i-2 (and further away) sometimes go straight through elements in column i-1
Also I can shift nodes only vertically, not horizontally because the amount of columns is limited.
To make it easier for myself I tried to place nodes in a grid-like pattern. And I've managed to group them by columns. But then I somehow need to iterate through columns and compare them with other columns to re-arrange stuff. And I don't know where to start.
UPD: I may be wrong but I feel like my problem with graph alignment is somehow related to a typical graph problem of the shortest path. Except that in my case there are multiple paths that should be calculated at the same time and some nodes can be passed only once.
On the image below you can see a nearly ideal redistribution that I made by just scribbling stuff on paper (direction left-to-right shows parent-to-child connections).
It is graph layout and drawing problem. You can take one of the following two approaches
Use already existing libraries: There are many graph layout libraries available for example GraphViz, Gephi, D3js etc. You can use theirs APIs directly or you can find applications/tools build on top of them. But to get best layout, you need to have guess on family of layout. e.g. Layered graph layout (Good for dense but layered graph like flowcharts) Tree layout (Used when graph is actually a tree or forest. There are many variants of tree), Radial tree layout (Again for tree but in polar system), Force directed layouts (When you don't know what visual structure will best represent the data, it is good starting point). All these layout will have many customization parameters like spacing between nodes, spacing between nodes and edges, overall aspect ratio of drawing etc.
GraphViz
Gephi
Implement layout algorithms yourself
Detailed coverage of graph drawing algorithms for different families can be found here
Graph Drawing Handbook
If you don't want to get into details, here are quick start points
For graphs, Do a topological sort and place nodes in layers as dictated by topo order. It can give you a very good starting point and help you avoid unnecessary crossings. Grid can be good idea here. But place nodes in grid in topo order.
Alternatively, Find a spanning tree for the graph, use tree layouts to draw spanning tree and then add remaining edges
For trees: Use recursive bottoms up approach for placing subtrees. For radial trees, do rectilinear layout and then transform to polar coordinate system
For unknown family: Use force directed method. Define force between two nodes (e.g. spring force) and then go through iterations to find equilibrium point.
Best auto visualization of a graph is very interesting area and people are trying many ML techniques here.
You could implement force directed drawing. Or you could use a graph drawing library that already supports force directed drawing, such as D3's force directed layout.
I have a dataset of DNA relationships (as a percent match) between myself and few hundred relatives, almost all distant relatives. I also have data on DNA relationships between each of them and certain other members in the dataset.
I'm hoping to build a network graph that shows the interrelationships and have Gephi build something that loosely resembles a family tree. But even using a small sample database I can't get the resulting graph to look anything like that.
I want each relationship (i.e. edge) to have a "force" related to the closeness of the relationship, so distant relatives (nodes) are pushed further away. I want the graph to self-assemble based on these "forces" and assume there is a layout for this, but I haven't found one.
I'm currently putting the DNA relationship in the weight column, and not using the interval column at all. But even using just 8 relatives and artificially perfect data I have to manually move nodes around to make it look remotely useful.
What layout should I use for this type of graph, and what other advice can you offer to make this work? Should the weight field increase or decrease as relationship distance increases?
… and have Gephi build something that loosely resembles a family tree. But even using a small sample database I can't get the resulting graph to look anything like that.
A family tree connects descendants (mostly). DNA similarity (as a percentage) does not conform to this structure. Related questions may be answered here.
Setting a Library > Edges > Edge Weight -filter to the DNA similarity attribute may help (but will not produce "something that loosely resembles a family tree").
I want each relationship (i.e. edge) to have a "force" related to the closeness of the relationship, so distant relatives (nodes) are pushed further away. I want the graph to self-assemble based on these "forces" …
All layouts work like that. However, Gephi does not feature hierarchical positioning. 3rd party candidates include EventGraphLayout, Layered Layout and Concentric Layout.
Should the weight field increase or decrease as relationship distance increases?
The greater an edge's weight, the stronger its connection (resulting in less distance between the nodes it connects). To a family tree however this is irrelevant.
I'm hoping to build a network graph that shows the interrelationships between each member …
What layout should I use for this type of graph, and what other advice can you offer to make this work?
Following steps emphasize clustering and modularity:
Calculate modularity.
Color nodes by modularity class: Appearance > Nodes > Partition > Modularity Class
Apply a layout; ForceAtlas 2 for example (with Dissuade Hubs, LinLog mode and Prevent Overlap enabled).
Apply the Contraction layout afterwards if necessary. Optionally set node size according to (for example) Eigenvector Centrality (prior to applying layout).
I am working on PPI network and for the network plot I am using Gephi and rgexf package. In my network there are 9453 no. of vertices and 36888 connections. So, when plot the network on gephi we can't analyse anything, so I plot only top 50 nodes network. Now I want to highlight that 50 node's network over the top 100 nodes network. Here the top means the nodes which has highest degree. So is there any way to do it?
This the plot for top 50 nodes, there is one node which is not connected to any of the node in top50:
I am struggling from few days.
UPDATE - I updated the descriptions
Ok so this is not totally trivial but not straightforward either. I have created a random network with 500 nodes to show you an example.
After you load your network with Gephi you need at least to run the Average degree from the statistics.
You then go to the Filters section, select Attributes>Range>In-degree, double-click and press the Filter button (Note: In your case you should just see Degree but the rest still apply)
Move the slider to the right so that in the Context panel the number of remaining nodes reaches ~50
Here is how it looks for me
COLORING:
While you keep the Filter button pressed you can apply a different color to the filtered nodes. In my case I went in Ranking>Nodes selected InDegree and applied a green color. Note that you can also select Degree to get the effect you want. The respective panel looks like that:
This colors only the filtered nodes. Alternatively, you can apply the node brush but then it becomes a bit dull :). There is a 3rd option but I leave it as an exercise :).
The final graph looks like that for me.
Programmatically you could do something similar in igraph. You can select the nodes with top-50 indegrees, assign them a color, assign all the rest a different color and export to GraphML.
I hope it helped.
What you can do is importing the network to gephi ASIS and compute degree centrality, this will generate a variable with the degree of each vertex in the graph. After that you can use the filter tool (as illustrated in this presentation http://www.clementlevallois.net/gephi/tuto/en/gephi_advanced%20functions_en.pdf) to restrict the number of vertices to work with using the degree variable. That's the easy way.
Alternatively, you can calculate each node's degree using either igraph or sna packages, and include such as an attribute of your network in the write.gexf function.
I need to visualize a relatively large graph (6K nodes, 8K edges) that has the following properties:
Distinct Clusters. Approximately 50-100 Nodes per cluster and moderate interconnectivity at the cluster level
Minimal (5-10 inter-cluster edges per cluster) interconnectivity between clusters
Let global edge overlap = The edge overlaps caused by directly visualizing a graph of Clusters = {A, B, C, D, E}, Edges = {Pentagram of those clusters, which is non-planar by the way and will definitely generate edge overlap if you draw it out directly}
Let Local Edge Overlap = the above but { A, B, C, D, E } are just nodes.
I need to visualize graphs with the above in a way that satisfies the following requirements
No global edge overlap (i.e. edge overlaps caused by inter-cluster properties is not okay)
Local edge overlap within a cluster is fine
Anyone have thoughts on how to best visualize a graph with the requirements above?
One solution I've come up with to deal with the global edge overlap is to make sure a cluster A can only have a max of 1 direct edge to another cluster (B) during visualization. Any additional inter-cluster edges between cluster A -> C, A -> D, ... are disconnected and additional node/edges A -> A_C, C -> C_A, A -> A_D, D -> D_A... are created.
Anyone have any thoughts?
Prefuse has some good graph drawing link text algorithms built in and it seems to handle fairly large graphs relatively well. You might try Flow Map Layout which is built on top of Prefuse.
Given your objectives, I think that the Fruchterman-Reingold algorithm does a pretty decent job of preventing edge overlap. See for example this screenshot of a network consisting of multiple components drawn using the Fruchterman-Reingold algorithm. IGraph has built-in support for this algorithm (as does Networkx I believe) and is really fast.
There is a program built on top of Prefuse called SocialAction. You have to request the code from the author, but it does a lot of statistical analysis on the graph for you, such as identifying subgraphs. I've used it on a graph with more than 18,000 nodes, and although it is very slow at that scale it still works.
Although it may be silly to ask at this point, had you tried out http://www.graphviz.org/ ?
I haven't seen too many graph visualization tools that support separating clusters within a graph visually. One option might be to take a look at WilmaScope. It looks to have some support for cluster based layouts.
Organic layout manages fairly well clustered graphs in yFiles framework. Try first in yEd to see if it does what needed. It is probably reasonable to use nested graphs alias groups for each cluster. Organic layout has feature called Group Layout Policy which can be used if layout needs to be done using different principles for inter-cluster and intra-cluster edges, with incremental layouting. With some effort, one can translate graph into GraphML to avoid manual work.