Social-Network-Analysis | Social Network Analysis Social Network Analysis | Social Channel Utils library

by Br1an6 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Social-Network-Analysis Summary

Social-Network-Analysis is a Python library typically used in Telecommunications, Media, Advertising, Marketing, Utilities, Social Channel Utils applications. Social-Network-Analysis has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Social-Network-Analysis build file is not available. You can download it from GitHub.

Social Network Analysis Social Network Analysis using Python with SciKit, Networkx ... etc.

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Social-Network-Analysis has a low active ecosystem.

It has 7 star(s) with 2 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Social-Network-Analysis has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Social-Network-Analysis is current.

Quality

Social-Network-Analysis has no bugs reported.

Security

Social-Network-Analysis has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Social-Network-Analysis is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Social-Network-Analysis releases are not available. You will need to build from source code and install.

Social-Network-Analysis has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed Social-Network-Analysis and discovered the below as its top functions. This is intended to give you an instant insight into Social-Network-Analysis implemented functionality, and help decide if they suit your requirements.

Evaluates the logistic regression
Fixture of a movie
Tokenize a string
Compute the accuracy of the cross validation
Computes the accuracy between the predicted and predicted and predicted probabilities
Partition a graph using a Girvan new graph
Estimate the distance between two nodes
BFS breadth - first search
Calculates the predictions for each movie
Cosity cosine similarity
Parse test data
Reads data from file
Score a graph based on max_depths
Split the data into train and test sets
Downloads the tarball
Plot accuracy
Read data from file
Calculates the best classifier from documents
Computes the accuracy between the predicted and predicted values
Return the top terms of the vocab
Evaluate the correlation coefficient
R Return a subset of a graph
Create a networkx graph
Generate a training graph
Compute the path score for a given node
Prints the summary of each test in test_docs
Compute the jaccard similarity of a node
Calculate mean accuracy per set
Creates a feature matrix

Get all kandi verified functions for this library.

Social-Network-Analysis Key Features

No Key Features are available at this moment for Social-Network-Analysis.

Social-Network-Analysis Examples and Code Snippets

No Code Snippets are available at this moment for Social-Network-Analysis.

Community Discussions

Trending Discussions on Social-Network-Analysis

R: K Means Clustering vs Community Detection Algorithms (Weighted Correlation Network) - Have I overcomplicated this question?

Neo4j chyper game of thrones

QUESTION

R: K Means Clustering vs Community Detection Algorithms (Weighted Correlation Network) - Have I overcomplicated this question?

Asked 2020-Nov-27 at 20:08

I have data that looks like this: https://imgur.com/a/1hOsFpF

The first dataset is a standard format dataset which contains a list of people and their financial properties.

The second dataset contains "relationships" between these people - how much they paid to each other, and how much they owe each other.

I am interested learning more about network and graph based clustering - but I am trying to better understand what type of situations require network based clustering, i.e. I don't want to use graph clustering where its not required (avoid a "square peg round hole" type situation).

Using R, first I created some fake data:

...

ANSWER

Answered 2020-Nov-27 at 15:29

I am trying to better understand what type of situations require network based clustering

This is completely dependent on your problem domain and the questions you are asking. You really need to have focused questions about the data that you are trying to answer. That being said, there is an set of clustering techniques you can apply that can use both edge weights and node attributes: Hierarchical Clustering.

Edge and node attributes come into play in how you determine the similarity/dissimilarity matrix which drives the clustering. Note that there are many, many implementations of this, take your time and find one that you can apply to your data and problem set.

Source https://stackoverflow.com/questions/64849921

QUESTION

Neo4j chyper game of thrones

Asked 2018-Jan-02 at 23:17

I have a question. So I was visiting this site and I am learning neo4j, and I have a question about this clause: (this is the site: game of thrones site)

...

ANSWER

Answered 2018-Jan-02 at 23:17

It is not clear what you are looking for. Here are two possible answers:

To find the average weightedDegree value (across all characters):

Source https://stackoverflow.com/questions/48069036

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Social-Network-Analysis

You can download it from GitHub.
You can use Social-Network-Analysis like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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