Network Analysis Resources & Updates
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🎞 Machine Learning with Graphs

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

πŸ’₯Graphs are a general language for describing and analyzing entities with relations/interactions. There are many types of networks and graphs, such as social networks, communication and transaction networks, biomedine networks, brain networks, etc. In this course, we will take advantage of relational structure for better prediction.


πŸ“½ Watch

πŸ“œ Slides

πŸ“²Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning
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πŸ“„Consensus clustering in complex networks

πŸ“˜Journal: Scientific Reports(I.F=5.516)

πŸ—“Publish year: 2012

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Consensus_clustering
πŸ‘1
πŸ“„Network analysis approach to Likert-style surveys

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Journal: PHYSICAL REVIEW PHYSICS EDUCATION RESEARCH (I.F=2.359)

πŸ—“Publish year: 2022

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Likert_style #survey
πŸ“„Motif discovery algorithms in static and temporal networks: A survey

πŸ“˜Journal: Journal of Complex Networks(I.F=2.011)

πŸ—“Publish year: 2020

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Motif #survey
πŸ‘2
🎞 Closeness Centrality & Betweenness Centrality: A Social Network Lab in R for Beginners

πŸ’₯Free recorded course

πŸ’₯So what then is β€œcloseness” or β€œbetweenness” in a network? How do we figure these things out and how do we interpret them? This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center.


πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis

#video #course #Closeness_Centrality #Betweenness_Centrality #code #R
πŸ“„Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale

πŸ“˜Journal: PLOS ONE(I.F=3.752)

πŸ—“Publish year: 2016

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #Clustering
πŸ“„A survey of game theory as applied to social networks

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Journal: T singhua Science and Technology (I.F=3.515)

πŸ—“Publish year: 2020

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #game_theory #survey
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🎞 Network Analysis in Systems Biology

πŸ’₯Free recorded course by Avi Ma’ayan, PhD

πŸ’₯An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, math, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for researchers who encounter large datasets in their own research. The course presents software tools developed by the Ma’ayan Laboratory

πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis

#video #course #Biology
🎞 Consul and Complex Networks

πŸ’₯Free recorded course by James Phillips, Consul Lead at HashiCorp

πŸ’₯A systematic overview of Consul's different network models, how they work, what kind of use cases they serve, and how prepared queries can help provide glue to keep service discovery simple across all.

πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis

#video #course #Consul
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πŸ“„Network-based machine learning and graph theory algorithms for precision oncology

πŸ“˜Journal: npj Precision Oncology(I.F=10.092)

πŸ—“Publish year: 2017

πŸ“ŽStudy paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #machine_Learning #graph
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
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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