awesome-network-analysis - A curated list of awesome network analysis resources.

•    R

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An awesome list of resources to construct, analyze and visualize network data. Inspired by Awesome Deep Learning, Awesome Math and others.

network-analysis network-visualization complex-networks political-networks semantic-networks graph-theory disease-networks network-science social-networks social-network-analysis historical-networks sna awesome-list awesome list

Obsei - Low code AI powered automation tool

•    Python

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Obsei is a low code AI powered automation tool. It can be used in various business flows like social listening, AI based alerting, brand image analysis, comparative study and more. It consist of Observer, Analyzer and Informer. Observer observes the platform like Twitter, Facebook, App Stores, Google reviews, Amazon reviews, News, Website etc and feed that information. Analyzer performs text analysis like classification, sentiment, translation, PII on the analyzed data. Informer sends it to ticketing system, data store, dataframe etc for further action and analysis.

nlp workflow natural-language-processing sentiment-analysis text-classification customer-support text-analysis artificial-intelligence text-analytics social-network-analysis workflow-automation low-code anonymization issue-tracking-system process-automation customer-engagement lowcode business-process-automation social-listening

randomgraph.js - Simple library for generating random graphs

•    Javascript

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A small library for generating, well, random graphs. Not that many models at the moment, feel free to jump in and add some more. The algorithms were partly ported and double-checked with the Gephi implementations (thx @jacomyal for the link).

graph network random sna social-network-analysis erdős–rényi watts-strogatz barabási–albert

big-data-upf - RECSM-UPF Summer School: Social Media and Big Data Research

•    HTML

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Citizens across the globe spend an increasing proportion of their daily lives online. Their activities leave behind granular, time-stamped footprints of human behavior and personal interactions that represent a new and exciting source of data to study standing questions about political and social behavior. At the same time, the volume and heterogeneity of web data present unprecedented methodological challenges. The goal of this course is to introduce participants to new computational methods and tools required to explore and analyze Big Data from online sources using the R programming language. We will focus in particular on data collected from social networking sites, such as Facebook and Twitter, whose use is becoming widespread in the social sciences. There are two ways you can follow the course and run the code contained in this GitHub repository. The recommended method is to connect to the provided RStudio server where all the R packages have already been installed, and all the R code is available. To access the server, visit bigdata.pablobarbera.com and log in with the information provided during class.

social-media big-data twitter facebook text-analysis social-network-analysis rstudio scraping-websites

instagraph - Social graph network of your Instagram account.

•    Go

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Social graph network of your Instagram account. Another branch named as 'sigma' is ready to use. Master branch is using neo4j as graph database.

social-graph instagram-graph instagram-scraper social-network-analysis graph-database

scala-github-relationship - A graphical view of the relationships between github users.

•    Scala

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A graphical view of the relationships between github users. In the picture, the login name of the user is gaocegege.

spider github social-network-analysis

learning-social-media-analytics-with-r - This repository contains code and bonus content which will be added from time to time for the book "Learning Social Media Analytics with R" by Packt

•    R

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The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights.

social-media social-network-analysis social-data analytics facebook twitter github foursquare r stackoverflow stackexchange flickr news guardian sentiment-analysis text-mining ggplot2 topic-modeling