📕Introduction to R for Data Science: A LISA 2020 Guidebook

📝Authors: Jacob D. Holster

💥This guidebook aims to provide readers an opportunity to make a start towards learning R for a variety of data science tasks, include (a) data cleaning and preparation, (b) statistical analysis, (c) data visualization, (d) natural language processing, (e) network analysis, and (f) Structural Equation Modeling to name a few. In Chapters 1 and 2 we invite readers to install R and RStudio and to start manipulating data for analysis. Chapter 3 and Chapter 4 include introductory exercises to teach data visualization and statistical analysis in R. In Chapter 5 and beyond, you will explore basic analytic concepts (e.g., correlation and regression) and more advanced approaches to data modeling through the lenses of Structural Equation Modeling, Network Analysis, and Text Analysis.

📚Free online guidebook
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📕Social Network Analysis

📝Authors: Stéphane Tufféry

💥Social networks are at the heart of big data, with their huge quantities of data of all kinds, text, images, video, and audio. Graphs are used to represent social networks in particular and all networks in general. In many applications of social networks, it is important to identify the most influential individuals. In a graph, the importance of a vertex can be expressed in several ways, the main ones being the degree centrality, the closeness centrality, the betweenness centrality, and prestige. A clique is a graph in which all vertices are connected and a quasi-clique is a group of vertices that are highly connected. A community is a subgraph that is both a quasi-clique and a quasi-connected component.

🗓 publish year: 2022
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📕Introduction to Neural Networks Using PyTorch

📝Authors: Pradeepta Mishra

💥Deep neural network–based models are gradually becoming the backbone for artificial intelligence and machine learning implementations. The future of data mining will be governed by the usage of artificial neural network–based advanced modeling techniques. One obvious question is why neural networks are only now gaining so much importance, because they were invented in 1950s.

🗓 publish year: 2022
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📕Financial Crisis and Global Governance: A Network Analysis

📝Author: Andrew Sheng

💥This chapter attempts to use network theory, drawn from recent work in sociology, engineering, and biological systems, to suggest that the current crisis should be viewed as a network crisis. Global fi nancial markets act as complex, scale-free, evolving networks that possess key characteristics requiring network management if they are to function with stability.

🗓 publish year: 2010
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📕Networks, Crowds, and Markets:
Reasoning About a Highly Connected World

📝Authors: David Easley and Jon Kleinberg.

💥Networks, Crowds, and Markets combines different scientific perspectives in its approach to understanding networks and behavior. Drawing on ideas from economics, sociology, computing and information science, and applied mathematics, it describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.

🗓 publish year: 2010
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📕Network visualization with R

💥This is a comprehensive tutorial on network visualization with R. It covers data input and formats, visualization basics, parameters and layouts for one-mode and bipartite graphs; dealing with multiplex links, interactive and animated visualization for longitudinal networks; and visualizing networks on geographic maps. To follow the tutorial, download the code and data below and use R and RStudio. You can also check out the most recent versions of all my tutorials here.

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📕Exploratory Social Network Analysis with Pajek

💥The book "Exploratory Social Network Analysis with Pajek" by Wouter De Noy, Andrey Mrvar and Vladimir Batagel is dedicated to teaching social network analysis, visualization and application of this knowledge in Pajek. Ultimately, readers will gain the knowledge, skills, and tools to apply social network analysis to a variety of disciplines.

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📕Python for Graph and Network Analysis

🗓Publish year: 2017

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📕From Social Networks to Time Series: Methods and Applications


📝Authors: Tongfeng Weng, Yaofeng Zhang, Pan Hui.

🗓 publish year: 2017
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📕Transportation Network Analysis

🗓Publish year: 2022

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📕Network Analysis: Integrating Social Network Theory, Method, and Application with R

🗓Publish year: 2023

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📚 Knowledge Graphs

✨This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale.

🧑‍💼 authors: Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, Jose Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M Rashid, Anisa Rula, Juan Sequeda, Lukas Schmelzeisen, Steffen Staab, Antoine Zimmerman
🗓Publish year: 2021

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📕Graph Representation Learning

💥Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial if we want systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D-vision, recommender systems, question answering, and social network analysis.

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📕Network and Content Analysis in an Online Community Discourse

💥The aim of this paper is to study interaction patterns among the members of a community of practice within the Dutch police organization and the way they share and construct knowledge together. The online discourse between 46 members, using First Class, formed the basis for this study. Social Network Analysis and content analysis were used to analyze the data. The results show that the interaction patterns between the members are rather centralized and that the network is relatively dense. Most of the members are involved within the discourse but person to person communication is still rather high. Content analysis revealed that discourse is focused on sharing and comparing information.

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📚 Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
💥 Book Chapter from IMIA Yearbook of Medical Informatics

🗓Publish year: 2023

🧑‍💻Authors: Fang Li , Yi Nian , Zenan Sun , Cui Tao
🏢University: University of Texas Health Science Center at Houston, USA

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📕Learning Analytics Methods and Tutorials

🗓Publish year: 2024

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📚 A curated list of awesome network analysis resources
💥 GitBook website

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📚 A Simple Introduction to Graph Theory
💥Booklet

🗓Publish year: 2024

🧑‍💻Author: Brian Heinold
🏢University: Mount Saint Mary's University, USA

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