I have a question regarding passthrough mapping on Cytoscape.
Let us say I have my nodes that belong to discreet groups. Those groups appear on a different column that I call Cat. How can I make the node fill colour to be according to Cat? I know I can do it with discrete mapping, choosing the Cat colours individually, but what if I have loads of Cats? When I choose Passthrough mappping, which I do not know how it works, nothing happens.
Thanks for your help.
Best,
David R.
Passthrough mapping does sort of what it sounds like -- it maps the value in the column cell to the visual attribute. In this case, you are mapping whatever is in Cats to a Color. Now, if you have a value of "Group 1", and Cytoscape maps it to a color, you wind up with ... nothing since Cytoscape doesn't know how to do that mapping. My suggestion would be to use discrete mapping, but let Cytoscape choose the colors. If you right-click on the "Discrete Mapping" and then go to "Mapping Value Generators" you'll see a number of options for automatically assigning colors.
-- scooter
Related
This is a project in really early phase and I'm trying to find ideas on where to start.
Any help or pointers would be greatly appreciated!
My problem:
I have text on one side, and a list of named GraphDB elements on the other (usually the name is either an acronym or a multi-word expression). My texts are not annotated.
I want to detect whenever a name is explicitly used in the text. The trick is that it will not necessarily be a perfect string match (for example an acronym can be used to shorten a multi-word expression, or a small part can be left out). So a simple string search will not have a 100% recall (even though it can be used as a starter).
If I just had an input and I wanted it to match it to one of the names, I would do a simple edit distance computation and that's it. What bugs me is that I have to do this for a whole text, and I don't know how to approach/break down the problem.
I cannot break down everything in N-grams because my named entities can be a single word or up to seven words long... Or can I?
I have thousands of Graph elements so I don't think NER can be applied here... Or can it?
An example could be:
My list of names is ['Graph Database', 'Manager', 'Employee Number 1']
The text is:
Every morning, the Manager browse through the Graph Database to look for updates. Every evening, Employee 1 updates the GraphDB.
I want in this block of text to map the 4 highlighted portions to their corresponding item in the list.
I have a small background in Machine Learning but I haven't really ever done NLP. To be clear, I do not care about the meaning of these words, I just want to be able to detect them.
Thanks
I am going over this YouTube tutorial, "Using LOAD CSV in the Real World".
The tutorial shows how to take a CSV, where each row is a complaint made against some bank, and model it as a Neo4j dictionary.
When doing so, the narrator sets Properties on the Complaint node:
CREATE (complaint:Complaint {id: line.`Complaint ID`})
SET complaint.year= TOINT(date[2]),
complaint.month= TOINT(date[0]),
complaint.day = TOINT(date[1])
I'm confused about a small point -- what makes this date information more of a 'Property' than a Label?
Could this be modeled instead where the node has this information encapsulated as Labels instead of Properties? At what point do you need one of these and not the other?
Labels and properties are very different things.
A property belongs to a node or a relationship, and has a name and a value.
A node label is similar in concept to a "class name", and has no value.
So, it does not make any sense to talk about putting a date value in a "label". You can only put a value in a property.
Note, however, that people often use a label name (e.g., "Foo") as a shorthand for "node that has the Foo label". For example, they may say "store the date in Foo" when they actually mean "store the date in the appropriate property of a node with the label Foo". Perhaps this is what is causing the confusion.
As cybersam pointed out in his answer, labels cannot contain values. They are just... labels. Like a tag. Taking this in a slightly different direction:
A long, long time ago, in a version far, far away, Neo4j didn't have labels. So, if you wanted to identify a particular type of node (say... a Person)... you'd likely include a property+value such as nodeType = 'Person'. And then you'd include a filter in your queries, such as:
WHERE node.nodeType = 'Person'
Labels make such a property type obsolete, and are also indexable. Further, you may have multiple labels on a node (which would require your legacy nodeType property to be an array, and not as efficient to search).
So: Labels for tagging/indexing. Properties for holding values.
" Node labels serve as an anchor point for a query. By specifying a label, we are specifying a subset of one or more nodes with which to start a query. Using a label helps to reduce the amount of data that is retrieved." https://graphacademy.neo4j.com/
I want to change the color of my nodes based on their properties:
Say I have many "Person" nodes. And I want those who live in New York to be red and those who live in Los Angeles to be blue. How would I write that. In cypher or in py2neo?
The styling of nodes and relationships in Neo4j Browser is controlled by a graph style sheet (GRASS), a cousin of CSS. You can view the current style by typing :style in the browser. To edit it, you can click on nodes and relationships and pick colors and sizes, or you can view the style sheet (:style), download it, make changes, and drag-n-drop it back into the view window.
Unfortunately for your case, color can only be controlled a) for all nodes and all relationships or b) for nodes by label and relationships by type. Properties can only be used for the text displayed on the node/rel.
It is not possible to interact with neo4j browser pro-grammatically. But the end goal could be achieved through a hack.
Even though I am a bit late here want to help others who might be finding a way. It is not possible to change the color of the nodes based on the property but there is a way it can be achieved by creating nodes based on the property. Keep in mind that after applying these queries your data wont be the same. So it is always a good idea to keep a backup of your data.
This is how labels are colored by default (Before):
Color based on the property
Suppose there is a label called Case with a property nationality and you want to color the nodes based on nationality. So following query could be used to create labels out of nationality property. For this you will need to install apoc library. check here for installation.
// BY NATIONALITY
MATCH (n:Case)
WITH DISTINCT n.nationality AS nationality, collect(DISTINCT n) AS persons
CALL apoc.create.addLabels(persons, [apoc.text.upperCamelCase(nationality)]) YIELD node
RETURN *
This will return all the people by nationality. Now you can color by country of nationality. Below shows an example.
Color based on the property and load with other labels
Lets say you also have a label called Cluster.The cases are attached to clusters via relationships. Just change the query to following to get the clusters with their relationships to cases.
//BY NATIONALITY WITH CLUSTERS
MATCH (n:Case),(c:Cluster)
WITH DISTINCT n.nationality AS nationality,
collect(DISTINCT n) AS persons,
collect(DISTINCT c) AS clusters
CALL apoc.create.addLabels(persons, [apoc.text.upperCamelCase(nationality)]) YIELD node
RETURN *
It will return cases and clusters with all the relationships. Below shows example.
Please leave an up vote if this was helpful and want to let others know that this is an acceptable answer. Thank you.
You cannot include formatting of the output in Cypher queries in the neo4j browser. Currently, the only way is to change the graph view manually or load a graph style file.
See tutorial here: http://neo4j.com/developer/guide-neo4j-browser/
Also, you cannot interact with the neo4j browser from py2neo.
If you are happy setting the color through a graphical user interface rather than programatically, Neo4j also supplies a data exploration addon named bloom. When using this addon (now automatically installed when using neo4j desktop), it is possible to set node color based on its properties.
In the example below, movies released after 2002 are colored green.
I am trying to use Gephi to help graph interview analysis results. The relationship map is only used to describe conventional connections and life cycles. What I would like to do is to size the nodes based on the number of interview responses that talk about the node, not the number of connections it has or the weighting of those connections. Can Gephi do this and if so, how do I do it please?
I have loaded in node weightings and can see this as part of node labels, but haven't been able to find a way of this having an effect on node size.
Many thanks
Data input field - change input format to integer
You can load the graph in gexf format adding a float attribute and add this attribute to ALL the nodes. It would like something like:
```
...
...
```
Once imported in Gephi, just go to the appearance tab and it will appear as one more attribute in "ranking" drop-down list.
If any problem with gefx format, let me know and I'll will share a whole example (just trying to remain short :-)
Regards
I have some hundreds of images which need to be grouped together. All the images have names in it along with colors. Is there an easiest way to group them based on the names inside along with the colors? Are there any packages available in Python or any algorithms with which this could be done?
For Example the image above has "boy" in it. If I had another similar image with the same name in it.Then how can I group them together.
If the text is as clear as this you might not even need machine learning: just group all the items with the same name in a dictionary using the name as the key. If the text is still clear but you want to group conjugates of name stem or lemmatize them with NLTK. If the text is clear but you want to group semantically related words that are not mere conjugates use a topic model or word2vec, which gives you a vector space embedding of each word you can then use to perform a similarity search.
I've highlighted the key terms to help you help yourself. The technical term for your problem is called clustering.