Network Analysis Resources & Updates
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πŸ“„Complex network approaches to nonlinear time series analysis

πŸ“˜Journal: Physics Reports (I.F=25.6)

πŸ—“Publish year: 2019

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #time_series
🎞 Machine Learning with Graphs: Applications of Graph ML

πŸ’₯Free recorded course by Jure Leskovec, Computer Science, PhD

πŸ’₯Graph machine learning can be applied in many scenarios, including the tasks of node classification, link prediction, graph classification, etc. Machine Learning at different levels of graphs usually demonstrate powerful capability in many specific tasks in different fields, ranging from protein folding, drug discovery, to recommender system, traffic prediction, among various other tasks.


πŸ“½ Watch

πŸ“œ Slides

πŸ’»Codes: part1 part2

πŸ“²Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #code #python
πŸ‘2
πŸ“„New perspectives on analysing data from biological collections based on social network analytics

πŸ“˜
Journal: Scientific Reports (I.F=4.996)

πŸ—“Publish year: 2020

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #biological
πŸ“„Applications of network analysis to routinely collected health care data: a systematic review

πŸ“˜
Journal: Journal of the American Medical Informatics Association (I.F=7.942)

πŸ—“Publish year: 2018

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Applications #health_care #review
πŸŽ“Analysis of the Structural Properties and Scalability of Complex Networks

πŸ“˜A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA

πŸ—“Publish year: 2018

πŸ“ŽStudy dissertation

πŸ“±Channel: @ComplexNetworkAnalysis
#dissertation #scalability
🎞 A GENTLE INTRODUCTION TO THE WORLD OF NETWORK SCIENCE

πŸ’₯Free recorded webinar by Pedro Ribeiro

πŸ“½ Watch

πŸ“±Channel: @ComplexNetworkAnalysis

#video #networks_science
πŸ‘1
πŸ“„Multilayer Networks in a Nutshell

πŸ“˜
Journal: Annual Review of Condensed Matter Physics (I.F=23.978)

πŸ—“Publish year: 2018

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Multilayer_Networks
🎞 An Introduction to Social Network Analysis: Part 1

πŸ’₯Free recorded workshop on Social Network Analysis (SNA)

πŸ’₯ Part 1 of the workshop provides an introduction to social network concepts, theories, and substantive problems. A brief history of SNA is given

πŸ“½ Watch

πŸ“±Channel: @ComplexNetworkAnalysis

#video
πŸ“„Open Graph Benchmark: Datasets for Machine Learning on Graphs

πŸ’₯Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

πŸ—“Publish year: 2020

πŸ“Ž Study paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Graphs
πŸ‘1
πŸ“„Multilayer networks: aspects, implementations, and application in biomedicine

πŸ“˜
Journal: Big Data Analytics

πŸ—“Publish year: 2020

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #application #biomedicine
2018_Study_on_centrality_measures_in_social_networks_a_survey.pdf
1.2 MB
πŸ“„Study on centrality measures in social networks: a survey

πŸ“˜Journal: Social Network Analysis and Mining (I.F=3.868)

πŸ—“Publish year: 2018

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #survey #centrality
2015_Network_analysis_for_a_network_disorder_The_emerging_role_of.pdf
1.7 MB
πŸ“„Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy

πŸ“˜Journal:Epilepsy & Behavior (I.F=2.937)

πŸ—“Publish year: 2015

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #epilepsy
πŸ‘2
πŸ“„Graph Theory in the Information Age

πŸ’₯This article is based on the Noether Lecture given at the
AMS-MAA-SIAM Annual Meeting, January 2009, Washington D. C.

πŸ—“Publish year: 2010

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper
πŸ“„Structure and tie strengths in mobile communication networks

πŸ“˜Journal: PNAS (I.F=11.205)

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #mobile_communication
πŸ“„A Critical Review of Centrality Measures in Social Networks

πŸ“˜Journal: Business & Information Systems Engineering (I.F=4.532)

πŸ—“Publish year: 2010

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #Review #Centrality
🎞 Machine Learning with Graphs: Choice of Graph Representation

πŸ’₯Free recorded course by Jure Leskovec, Computer Science, PhD

πŸ’₯One essential task to consider before we conduct machine learning on graphs is to find an appropriate way to represent the graphs. What are the factors that will affect our choices as to the representations? In this video, we’ll be looking at the different approaches to abstracting graphs: directed vs. undirected, weighted vs. unweighted, homogeneous vs bipartite, and so on.


πŸ“½ Watch

πŸ“œ Slides

πŸ“²Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning