Applied Social Network Analysis in Python

Start Date: 11/29/2020

Course Type: Common Course

Course Link:

About Course

This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

Course Syllabus

Module One introduces you to different types of networks in the real world and why we study them. You'll learn about the basic elements of networks, as well as different types of networks. You'll also learn how to represent and manipulate networked data using the NetworkX library. The assignment will give you an opportunity to use NetworkX to analyze a networked dataset of employees in a small company.

Coursera Plus banner featuring three learners and university partner logos

Course Introduction

Applied Social Network Analysis in Python This course provides an introduction to applied social network analysis and statistics. We will cover topics such as statistical inference, iterative methods, and machine learning. We will use Python packages pandas, nltk, and scikit-learn to implement our model. We will also cover practical exercises and applications of the model. Upon completing this course, you will be able to: 1. Explain the purpose and basics of social network analysis 2. Leverage the statistical tools provided by the package 3. Use and extend from and between pandas, scikit-learn and nltk 4. Model and network a social network 5. Apply linearization and applications to a simple case study This course is part of the 5-course Specialization “Applied Social Network Analysis in Python”. Interested in earning 3 university credits from the University of London to study in Python, as well as getting a verified certificate from the top ranked Python software package in the world, Python-NLP? If so check out "How you can earn 3 university credits from the University of London to study in Python, and get a verified certificate from the top ranked Python software package in the world, Python-NLP" for additional information.You will need to register for a student account on Coursera. Once you have registered, you will be able to login using your university account and earn 3 credits towards a future course

Course Tag

Graph Theory Network Analysis Python Programming Social Network Analysis

Related Wiki Topic

Article Example
Social network analysis Social network analysis is used extensively in a wide range of applications and disciplines. Some common network analysis applications include data aggregation and mining, network propagation modeling, network modeling and sampling, user attribute and behavior analysis, community-maintained resource support, location-based interaction analysis, social sharing and filtering, recommender systems development, and link prediction and entity resolution. In the private sector, businesses use social network analysis to support activities such as customer interaction and analysis, information system development analysis, marketing, and business intelligence needs. Some public sector uses include development of leader engagement strategies, analysis of individual and group engagement and media use, and community-based problem solving.
Social network analysis software Commonly used and well-documented scripting tools used for network analysis include: NetMiner with Python scripting engine, the statnet suite of packages for the R statistical programming language, igraph, which has packages for R and Python, muxViz (based on R statistical programming language and GNU Octave) for the analysis and the visualization of multilayer networks, the NetworkX library for Python, and the SNAP package for large-scale network analysis in C++ and Python. Though difficult to learn, some of these open source packages are growing much faster in terms of functionality and features than privately maintained software, and extensive documentation and tutorials are available.
International Network for Social Network Analysis The International Network for Social Network Analysis (INSNA) is a professional academic association of researchers and practitioners of social network analysis. Members have interests in social networks as a new theoretical paradigm, in methodological developments, and in a variety of applications of different types of social networks approaches, social network software, and social networking.
Social network analysis software Social network analysis software (SNA software) is software which facilitates quantitative or qualitative analysis of social networks, by describing features of a network either through numerical or visual representation.
Social network analysis (criminology) Social network analysis in criminology views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as offender movement, co-offenders, crime groups, etc.) These networks are often depicted in a social network diagram, where nodes are represented as points and ties are represented as lines.
Social network analysis Social network analysis (SNA) is the process of investigating social structures through the use of network and graph theories. It characterizes networked structures in terms of "nodes" (individual actors, people, or things within the network) and the "ties", "edges", or "links" (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network analysis include social media networks, memes spread, friendship and acquaintance networks, collaboration graphs, kinship, disease transmission, and sexual relationships. These networks are often visualized through "sociograms" in which nodes are represented as points and ties are represented as lines.
Social network analysis Social network analysis has its theoretical roots in the work of early sociologists such as Georg Simmel and Émile Durkheim, who wrote about the importance of studying patterns of relationships that connect social actors. Social scientists have used the concept of "social networks" since early in the 20th century to connote complex sets of relationships between members of social systems at all scales, from interpersonal to international. In the 1930s Jacob Moreno and Helen Jennings introduced basic analytical methods. In 1954, John Arundel Barnes started using the term systematically to denote patterns of ties, encompassing concepts traditionally used by the public and those used by social scientists: bounded groups (e.g., tribes, families) and social categories (e.g., gender, ethnicity). Scholars such as Ronald Burt, Kathleen Carley, Mark Granovetter, David Krackhardt, Edward Laumann, Anatol Rapoport, Barry Wellman, Douglas R. White, and Harrison White expanded the use of systematic social network analysis. Even in the study of literature, network analysis has been applied by Anheier, Gerhards and Romo, Wouter De Nooy, and Burgert Senekal. Indeed, social network analysis has found applications in various academic disciplines, as well as practical applications such as countering money laundering and terrorism.
Social network analysis There are several key terms associated with social network analysis research in computer-supported collaborative learning such as: density, centrality, indegree, outdegree, and sociogram.
Social network analysis Social network analysis is also used in intelligence, counter-intelligence and law enforcement activities. This technique allows the analysts to map a clandestine or covert organization such as a espionage ring, an organized crime family or a street gang. The National Security Agency (NSA) uses its clandestine mass electronic surveillance programs to generate the data needed to perform this type of analysis on terrorist cells and other networks deemed relevant to national security. The NSA looks up to three nodes deep during this network analysis. After the initial mapping of the social network is complete, analysis is performed to determine the structure of the network and determine, for example, the leaders within the network. This allows military or law enforcement assets to launch capture-or-kill decapitation attacks on the high-value targets in leadership positions to disrupt the functioning of the network.
Social network analysis software Visual representations of social networks are important to understand network data and convey the result of the analysis. Visualization often also facilitates qualitative interpretation of network data. With respect to visualization, network analysis tools are used to change the layout, colors, size and other properties of the network representation. All of the tools above contain visualization capabilities. NetMiner, igraph, Cytoscape, muxViz and NetworkX have the highest level of functionality in terms of producing high-quality graphics.
Social network analysis The NSA has been performing social network analysis on call detail records (CDRs), also known as metadata, since shortly after the September 11 attacks.
Semantic social network In 2007 a team of French researchers at INRIA applied Semantic Social Network concepts and established formal methods for ontology matching. And in 2009 more researchers around the world started to imnplement Semantic Social Networks amongst them a team in Iran applied the concepts of Semantic Social Networks in order to facilitate organizational collaboration and expertise finding in decentralized organizations to Rayan Faragard, a software development company. They then performed social network analysis from the network they had gathered by FOAF tags which showed that using semantic social networks greatly increases the reliability, effectiveness and collaboration.
Social network analysis (criminology) The application of social network analysis during the collaboration between criminals and terrorists when both use smuggling tunnels was explored by Lichtenwald and Perri. Lichtenwald and Perri referenced many of the notable scholars and key papers in the field.
Social network analysis Visual representation of social networks is important to understand the network data and convey the result of the analysis. Numerous methods of visualization for data produced by social network analysis have been presented. Many of the analytic software have modules for network visualization. Exploration of the data is done through displaying nodes and ties in various layouts, and attributing colors, size and other advanced properties to nodes. Visual representations of networks may be a powerful method for conveying complex information, but care should be taken in interpreting node and graph properties from visual displays alone, as they may misrepresent structural properties better captured through quantitative analyses.
Social network Actor level: The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego". Egonetwork analysis focuses on network characteristics such as size, relationship strength, density, centrality, prestige and roles such as isolates, liaisons, and bridges. Such analyses, are most commonly used in the fields of psychology or social psychology, ethnographic kinship analysis or other genealogical studies of relationships between individuals.
Social network A social network is a social structure made up of a set of social actors (such as individuals or organizations), sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures. The study of these structures uses social network analysis to identify local and global patterns, locate influential entities, and examine network dynamics.
Social network analysis Social network analysis has emerged as a key technique in modern sociology. It has also gained a significant following in anthropology, biology, communication studies, economics, geography, history, information science, organizational studies, political science, social psychology, development studies, sociolinguistics, and computer science and is now commonly available as a consumer tool.
Social network analysis Large textual corpora can be turned into networks and then analysed with the method of social network analysis. In these networks, the nodes are Social Actors, and the links are Actions. The extraction of these networks can be automated, by using parsers.
Social network analysis Although many studies have demonstrated the value of social network analysis within the computer-supported collaborative learning field, researchers have suggested that SNA by itself is not enough for achieving a full understanding of CSCL. The complexity of the interaction processes and the myriad sources of data make it difficult for SNA to provide an in-depth analysis of CSCL. Researchers indicate that SNA needs to be complemented with other methods of analysis to form a more accurate picture of collaborative learning experiences.
Social network Few complete theories have been produced from social network analysis. Two that have are Structural Role Theory and Heterophily Theory.