Network Analysis Resources & Updates
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📄Recommending on Graphs: A Comprehensive Review from Data Perspective
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Review
📄Recommending on Graphs: A Comprehensive Review from Data Perspective
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Review
📄A Network Analysis of Twitter Interactions by Members of the U.S. Congress
📘Journal: ACM Transactions on Social Computing
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Twitter #Congress
📄What Is Graph Analytics & Its Top Tools
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
📄Social network analysis in operations and supply chain management: a review and revised research agenda
📘Journal: INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT (I.F=9.36)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #supply #chain_management #agenda #review
🎞 A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls
💥Free recorded tutorial by Andre M. Bastos
🔹This tutorial will review and summarize current analysis methods used in the field of invasive and non-invasive electrophysiology to study the dynamic connections between neuronal populations. First, will review metrics for functional connectivity, including coherence, phase synchronization, phase slope index, and Granger causality, with the specific aim to provide an intuition for how these metrics work, as well as their quantitative definition Next, will highlight a number of interpretational caveats and common pitfalls that can arise when performing functional connectivity analysis, including the common reference problem, the signal to noise ratio problem, the volume conduction problem, the common input problem, and the sample size bias problem. These pitfalls will be illustrated by presenting a series of MATLAB-scripts, which can be executed by the tutorial participants to simulate each of these potential problems. I will discuss how some of these issues can be addressed using current methods
📽Watch
📱Channel: @ComplexNetworkAnalysis
#video #Tutorial #Connectivity #review
📄Graph analysis to survey data: a first approximation
📘Journal: Complex Systems in Science (I.F=0.36)
🗓Publish year: 2015
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #survey
📄Using social network analysis to examine alcohol use among adults: A systematic review
📘Journal: PLOS ONE (I.F=3.752)
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #examine #alcohol #adults #review
📄What Is Graph Analytics?
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
📄Complex network analysis of time series
📘Journal: EPL (I.F= 1.947)
🗓Publish year: 2016
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Time_Series
📄Time Series Forecasting Based on Complex Network Analysis
📘Journal: IEEE Access (I.F= 4.809)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Forecasting #Time_Series
📄A Taxonomy and Survey of Dynamic Graph Visualization
📘Journal: Computer Graphics Forum (I.F= 1.6)
🗓Publish year: 2016
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey #Visualization
📄Graph Learning: A Survey
📘Journal: IEEE Transactions on Artificial Intelligence
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey
📄Survey of Attack Graph Analysis Methods from the Perspective of Data and Knowledge Processing
📘Journal: Security and communication networks (IF= 1.288)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Survey
📕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
📖 Study book
📲Channel: @ComplexNetworkAnalysis
#book #R #code
📄Blockchain Network Analysis: A Comparative Study of Decentralized Banks?
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Blockchain #Banks #review
📄Using Theory to Guide Exploratory Network Analyses
📘Journal: Faculty & Staff Research and Creative Activity
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
📄Social network analysis of open source software: A review and categorisation
📘Journal: INFORMATION AND SOFTWARE TECHNOLOGY (I.F=3.862)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #software #categorisation #review
🎞 Financial Network Analysis using Python | Kalyan Prasad | Conf42 Python 2022
💥Free recorded workshop by Kalyan Prasad Data Scientist & Analytics Manager at Creative Crewz.
💥Historically, networks have been studied extensively in graph theory, an area of mathematics. After many applications to several different subjects including physics, health science, and sociology, over the last years, network analysis has become an active topic not only in data science but also in finance. In a nutshell, a network is a system with nodes connected by linkages.
Network analysis is popular to describe the characteristics or behaviors of complex networks. There has been also some research conducted to model the stock market using networks. The motivation is that the performances of certain stocks are often correlated, either because of the general market direction or the cyclicity of the same segments of the market.
📝In this talk, they are going to cover the following points:
• Introduction to Networks
• History & why graphs
• Finance evolution in networks
• Understanding Network structure
• Leveraging the power of Python Graphs
• Real-time finance usage of network analysis using two examples(hands-on)
• Wrap-up
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #workshop #Graph #Python
📕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
📖 Study
💻 Code
📲Channel: @ComplexNetworkAnalysis
#book #R #code #video
