2020_Complex_spatial_networks_Theory_and_geospatial_applications.pdf
2.2 MB
πComplex spatial networks: Theory and geospatial applications
πJournal: Geography Compass (I.F= 4.833)
πPublish year: 2020
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #geospatial
πJournal: Geography Compass (I.F= 4.833)
πPublish year: 2020
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #geospatial
πGraph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks
πJournal: Multidisciplinary Digital Publishing Institute
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Applications #survey
πJournal: Multidisciplinary Digital Publishing Institute
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Applications #survey
πCyberbullying Detection in Social Networks: A Survey
πConference: 2nd International Conference on Communication & Information Processing (ICCIP) 2020ο»Ώ
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Cyberbullying #survey
πConference: 2nd International Conference on Communication & Information Processing (ICCIP) 2020ο»Ώ
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Cyberbullying #survey
The_Practitionerβs_Guide_To_Graph_Data_Applying_Graph_Thinking_And.pdf
25.4 MB
π The Practitioner's Guide to Graph Data: Applying Graph Thinking and Graph Technologies to Solve Complex Problems
πPublish year: 2020
π Study the book
π±Channel: @ComplexNetworkAnalysis
#book
πPublish year: 2020
π Study the book
π±Channel: @ComplexNetworkAnalysis
#book
Forwarded from Bioinformatics
π₯Good tutorial of Cytoscape for biological network visualization
π An Introduction to Network Analysis and Cytoscape
π Study the article
π²Channel: @Bioinformatics
π An Introduction to Network Analysis and Cytoscape
π Study the article
π²Channel: @Bioinformatics
π4
2018-Animal Social Networks.pdf
340.1 KB
πAnimal Social Networks
πJournal: Reference Module in Life Sciences (I.F= 6.780 )
πPublish year: 2018
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper
πJournal: Reference Module in Life Sciences (I.F= 6.780 )
πPublish year: 2018
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper
π1
πExploring and Analyzing Network Data with Python
π₯Technical paper
π₯A brief introduction with examples by python
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #python #code #NetworkX
π₯Technical paper
π₯A brief introduction with examples by python
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #python #code #NetworkX
programminghistorian.org
Exploring and Analyzing Network Data with Python
This lesson introduces network metrics and how to draw conclusions from them when working with humanities data. You will learn how to use the NetworkX Python package to produce and work with the...
π5
πJust like humans, dolphins have complex social networks
π₯They may not be on Facebook or Twitter, but dolphins do, in fact, form highly complex and dynamic networks of friends, according to a recent study. Dolphins are known for being highly social animals, and biologists took a closer look at the interactions between bottlenose dolphins in the Indian River Lagoon and discovered how they mingle and with whom they spend their time.
π Study
π²Channel: @ComplexNetworkAnalysis
#news
π₯They may not be on Facebook or Twitter, but dolphins do, in fact, form highly complex and dynamic networks of friends, according to a recent study. Dolphins are known for being highly social animals, and biologists took a closer look at the interactions between bottlenose dolphins in the Indian River Lagoon and discovered how they mingle and with whom they spend their time.
π Study
π²Channel: @ComplexNetworkAnalysis
#news
ScienceDaily
Just like humans, dolphins have complex social networks
They may not be on Facebook or Twitter, but dolphins do, in fact, form highly complex and dynamic networks of friends, according to a recent study. Dolphins are known for being highly social animals, and biologists took a closer look at the interactions betweenβ¦
πCentrality metrics in dynamic networks: a comparison study
πJournal: IEEE Transactions on network science and engineering (I.F: 5.033)
πPublish year: 2018
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper
πJournal: IEEE Transactions on network science and engineering (I.F: 5.033)
πPublish year: 2018
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper
π2
π Using NetLogo: Complex Problem Solving in Networks
π₯Free recorded Lecture
π₯How do revolutions emerge without anyone expecting them? How did social norms about same sex marriage change more rapidly than anyone anticipated? Why do some social innovations take off with relative ease, while others struggle for years without spreading? More generally, what are the forces that control the process of social evolution βfrom the fashions that we wear, to our beliefs about religious tolerance, to our ideas about the process of scientific discovery and the best ways to manage complex research organizations? The social world is complex and full of surprises. Our experiences and intuitions about the social world as individuals are often quite different from the behaviors that we observe emerging in large societies. Even minor changes to the structure of a social network - changes that are unobservable to individuals within those networks - can lead to radical shifts in the spread of new ideas and behaviors through a population. These βinvisibleβ mathematical properties of social networks have powerful implications for the ways that teams solve problems, the social norms that are likely to emerge, and even the very future of our society. This course condenses the last decade of cutting-edge research on these topics into six modules. Each module provides an in-depth look at a particular research puzzle -with a focus on agent-based models and network theories of social change -and provides an interactive computational model for you try out and to use for making your own explorations! Learning objectives - after this course, students will be able to... - explain how computer models are used to study challenging social problems - describe how networks are used to represent the structure of social relationships - show how individual actions can lead to unintended collective behaviors - provide concrete examples of how social networks can influence social change - discuss how diffusion processes can explain the growth social movements, changes in cultural norms, and the success of team problem solving
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Lecture #NetLogo
π₯Free recorded Lecture
π₯How do revolutions emerge without anyone expecting them? How did social norms about same sex marriage change more rapidly than anyone anticipated? Why do some social innovations take off with relative ease, while others struggle for years without spreading? More generally, what are the forces that control the process of social evolution βfrom the fashions that we wear, to our beliefs about religious tolerance, to our ideas about the process of scientific discovery and the best ways to manage complex research organizations? The social world is complex and full of surprises. Our experiences and intuitions about the social world as individuals are often quite different from the behaviors that we observe emerging in large societies. Even minor changes to the structure of a social network - changes that are unobservable to individuals within those networks - can lead to radical shifts in the spread of new ideas and behaviors through a population. These βinvisibleβ mathematical properties of social networks have powerful implications for the ways that teams solve problems, the social norms that are likely to emerge, and even the very future of our society. This course condenses the last decade of cutting-edge research on these topics into six modules. Each module provides an in-depth look at a particular research puzzle -with a focus on agent-based models and network theories of social change -and provides an interactive computational model for you try out and to use for making your own explorations! Learning objectives - after this course, students will be able to... - explain how computer models are used to study challenging social problems - describe how networks are used to represent the structure of social relationships - show how individual actions can lead to unintended collective behaviors - provide concrete examples of how social networks can influence social change - discuss how diffusion processes can explain the growth social movements, changes in cultural norms, and the success of team problem solving
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Lecture #NetLogo
Coursera
6.5 Using NetLogo: Complex Problem Solving in Networks - Problem Solving in Networks | Coursera
Video created by University of Pennsylvania for the ...
πHow to model a social network with R
π₯Technical paper
π₯A brief introduction with examples by R
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #R #code
π₯Technical paper
π₯A brief introduction with examples by R
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #R #code
Medium
How to model a social network with R
A practical introduction to network theory
π2
πA Comprehensive Survey on Community Detection with Deep Learning
πJournal: IEEE Transactions on Neural Networks and Learning Systems(I.F=14.255)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #community_detection #deep_learning #survey
πJournal: IEEE Transactions on Neural Networks and Learning Systems(I.F=14.255)
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #community_detection #deep_learning #survey
π1
2021_Nature_inspired_link_prediction_and_community_detection_algorithms.pdf
1.1 MB
πNature inspired link prediction and community detection algorithms for social networks: a survey
πJournal: International Journal of System Assurance Engineering and Management (I.F: 2.02)
πPublish year: 2021
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #survey #linkPrediction #communityDetection
πJournal: International Journal of System Assurance Engineering and Management (I.F: 2.02)
πPublish year: 2021
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #survey #linkPrediction #communityDetection
πChildrenβs Social Networks and Well-Being
πPublish year: 2014
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Well_Being
πPublish year: 2014
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #Well_Being
Graph Theory and Social Networks.pdf
973.1 KB
πGraph Theory and Social Networks
πBooklet: Kimball Martin
πPublish year: 2014
π²Channel: @ComplexNetworkAnalysis
#Booklet #Python #code
πBooklet: Kimball Martin
πPublish year: 2014
π²Channel: @ComplexNetworkAnalysis
#Booklet #Python #code
πOn community structure in complex networks: challenges and opportunities
πJournal: Applied Network Science (I.F: 2.65)
πPublish year: 2019
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper
πJournal: Applied Network Science (I.F: 2.65)
πPublish year: 2019
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper
π1
2021_Community_detection_in_complex_networks_From_statistical_foundations.pdf
5 MB
πCommunity detection in complex networks: From statistical foundations to data science applications
πJournal: WIREs Computational Statistics (I.F:3.282)
πPublish year: 2021
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #CommunityDetection
πJournal: WIREs Computational Statistics (I.F:3.282)
πPublish year: 2021
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #CommunityDetection