Repository for Social network analysis presented in the video link!

A Social Network Analysis of the League of Nations' Intellectual Cooperation

An analysis of 9 terrorist attack networks across various countries between the years 2000 and 2005 using a Gephi network graph visualization with the goal of discovering patterns, similarities, connections, and potential relationships between one another’s actors.

Empirical Analysis of Predictive Algorithms for Collaborative Filtering, constructing a Social Network using Twitter Data, Community Detection and Link Prediction using Facebook ‘Like’ Data, Categorizing Movie Reviews based on Sentiment Analysis, Content-based Recommendation Algorithm using Python, Pandas, Numpy and scikit-learn.

Analysis of Centrality -Finding Most Influential Person in a Social Network

Identify the counts of hashtags and mentioned accounts and display it as graph and wordcloud

🌐This repository contains solutions to the course Applied Social Network Analysis in Python by University of Michigan.

Social Network Analysis of the Tweeter (X) tweets, retweets

No description available

Status: Deprecated

No description available

Twitter network analysis using networkx and tweepy.

Social media such as Facebook, Twitter, LinkedIn, and Instagram led us to realize the importance of social network analysis. Social network analysis has its applications in social media networks, friendship (trust) network, disease transmission, and sexual relationships. In business, social network analysis is applied to fraud detection, viral marketing, terrorism and crime prevention, promising technology identification, and key player identification. For example, world leading companies seek for key scientific leaders and factors using social network analysis. The number of its applications is getting larger. Social network analysis is a special case of network analysis. The first step for network analysis is to define what objects you want to deal with and what relationships between the objects you want to focus on. How you define the objects and the relationships between them will decide the definition of your network. An object becomes a node in a network. The centrality of the node is determined by its relations to the other nodes. Analyzing different types of centrality will give you an idea on the position of the node among the others. The weight of an edge between two nodes represents the strength of their relation. The communities clustered by a machine learning algorithm may give you some hint to the abstractions and the notions of the whole network. This course will help students think a system including a social system, knowledge system, and communication system in relational and structural way that may lead the students to come up with insights. Bibliometrics (i.e., scholarly communication) and informetrics will be covered. Besides, technology and market will be examined by network analysis. Sample codes for network analysis will be given to the students in Java, Python, and R. Sample dataset for exercising the codes will be also given.

This repository is intended for laboratory work on course Analysis of Social Networks

This repository contains materials, assignments, and projects from my Social Network Analysis course as a fourth-year Data Science student.

No description available

Deep Learning - Convolutional Neural Networks (CNN) and social network analysis. Ponencia presentada en las XIII Jornadas de Sociologia. UBA, Agosto 2019

Course project for IIT CS579, Social Network Analysis

Measuring and managing network data; analyzing characteristics of individuals, relationships, and groups; predicting behaviours

Web scraping and web crawling using Scrapy to extract data from MyAnimeList.net to map a network and perform a Social Network Analysis using NetworkX.

This is a project of the module SAN of the University of Applied Science with the goal to analyze a Social Network.

No description available

No description available

No description available

Using Graphing techniques to find recommendations

Analysis of User Representations for Information Diffusion in Social Networks

A project of social network analysis where random data set is analysed and used to create network maps and graphs.

Analysis of European Union Mail dataset to compute degree centrality, betweenness, interaction and visualization using the R-shiny app.

Social network analysis tool which finds matches of a given query graph in a social network.

Numerous complex systems such as social networks can be modeled using graphs. Graphs used in social network mining are generally made up of numerous nodes (Facebook 1.15 billion, YouTube 1 billion on september 2013) and/or are very low dense. If the available architectures are not efficiently exploited, certain algorithms will have very long execution times. However, the evolution of architectures from mono-core through multi-cores to many-cores makes them complex thus rendering difficult the task of writing programs to be run on these machines. Looking for a language that can associate both facility and efficiency, we start proposing a DSL (Domain Specific Language) written and embedded in Erlang. This DSL should facilitates the programming of social network mining algorithms on multi-core and many-core platforms. So that writing parallel code on these platforms should be an easy task. In this file, we give some functions that should be useful for a social network analyzer (load_graph_gml ...). We propose an extra-operator (parallel_op) that help him to easily write parallel code without specifying messages and process. We will be happy to know that Erlang community will accept and continuous this project. Note that this kind of project is already done in Python. But because of its GIL (Global Interpreter Lock), Python doesn't take profit to the available multi/many-core to reduce execution time.