πComplex Network: Facebook friends list
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper
Medium
Complex Network: Facebook friends list
A small introduction to complex network analysis and the tools used.
π clustering and motifs
π₯Free recorded course
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #clustering #motif
π₯Free recorded course
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #clustering #motif
YouTube
Social Network Analysis || 02 5B clustering and motifs 20 45
Please subscribe to this channel for more updates!
πComplex network approaches to nonlinear time series analysis
πJournal: Physics Reports (I.F=25.6)
πPublish year: 2019
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #time_series
πJournal: Physics Reports (I.F=25.6)
πPublish year: 2019
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #time_series
π Machine Learning with Graphs: Applications of Graph ML
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯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.
π½ Watch
π Slides
π»Codes: part1 part2
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #code #python
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯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.
π½ Watch
π Slides
π»Codes: part1 part2
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #code #python
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.2 - Applications of Graph ML
For more information about Stanfordβs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2XQKRsU
Jure Leskovec
Computer Science, PhD
Graph machine learning can be applied in many scenarios, including the tasks of nodeβ¦
Jure Leskovec
Computer Science, PhD
Graph machine learning can be applied in many scenarios, including the tasks of nodeβ¦
π2
πNew perspectives on analysing data from biological collections based on social network analytics
πJournal: Scientific Reports (I.F=4.996)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #biological
πJournal: Scientific Reports (I.F=4.996)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #biological
π Methods in building and analysing biological networks
π₯Free recorded webinar series
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #webinar #biological_networks
π₯Free recorded webinar series
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #webinar #biological_networks
YouTube
Methods in building and analysing biological networks
Cells are complex and dynamic systems able to modify their behaviour and their morphology in response to internal or environment-induced cues. Such response is achieved thanks to enzymatic reactions and physical interactions that occur between cellular componentsβ¦
πApplications of network analysis to routinely collected health care data: a systematic review
πJournal: Journal of the American Medical Informatics Association (I.F=7.942)
πPublish year: 2018
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Applications #health_care #review
πJournal: Journal of the American Medical Informatics Association (I.F=7.942)
πPublish year: 2018
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Applications #health_care #review
πAnalysis of the Structural Properties and Scalability of Complex Networks
πA DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA
πPublish year: 2018
πStudy dissertation
π±Channel: @ComplexNetworkAnalysis
#dissertation #scalability
πA DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA
πPublish year: 2018
πStudy dissertation
π±Channel: @ComplexNetworkAnalysis
#dissertation #scalability
π A GENTLE INTRODUCTION TO THE WORLD OF NETWORK SCIENCE
π₯Free recorded webinar by Pedro Ribeiro
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #networks_science
π₯Free recorded webinar by Pedro Ribeiro
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video #networks_science
YouTube
DaSSWeb - 'A GENTLE INTRODUCTION TO THE WORLD OF NETWORK SCIENCE'
Pedro Ribeiro
Assistant Professor at the University of Porto
Assistant Professor at the University of Porto
π1
πMultilayer Networks in a Nutshell
πJournal: Annual Review of Condensed Matter Physics (I.F=23.978)
πPublish year: 2018
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Multilayer_Networks
πJournal: Annual Review of Condensed Matter Physics (I.F=23.978)
πPublish year: 2018
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #Multilayer_Networks
π An Introduction to Social Network Analysis: Part 1
π₯Free recorded workshop on Social Network Analysis (SNA)
π₯ Part 1 of the workshop provides an introduction to social network concepts, theories, and substantive problems. A brief history of SNA is given
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video
π₯Free recorded workshop on Social Network Analysis (SNA)
π₯ Part 1 of the workshop provides an introduction to social network concepts, theories, and substantive problems. A brief history of SNA is given
π½ Watch
π±Channel: @ComplexNetworkAnalysis
#video
πOpen Graph Benchmark: Datasets for Machine Learning on Graphs
π₯Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
πPublish year: 2020
π Study paper
π²Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Graphs
π₯Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
πPublish year: 2020
π Study paper
π²Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Graphs
π1
π Methods Matter - Social Network Analysis
π₯ free recorded podcast on Social Network Analysis
π§ listen
π±Channel: @ComplexNetworkAnalysis
#podcast
π₯ free recorded podcast on Social Network Analysis
π§ listen
π±Channel: @ComplexNetworkAnalysis
#podcast
YouTube
Methods Matter - Social Network Analysis
The Methods Matter Podcast - from Dementia Researcher & the National Centre for Research Methods. A podcast for people who don't know much about methods...those who do, and those who just want to find news and clever ways to use them in their research.
Inβ¦
Inβ¦
π1
πMultilayer networks: aspects, implementations, and application in biomedicine
πJournal: Big Data Analytics
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #application #biomedicine
πJournal: Big Data Analytics
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #application #biomedicine
π Network Motifs
π₯Free recorded course
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #clustering #motif
π₯Free recorded course
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course #clustering #motif
YouTube
24 - Network Motifs
Motif, null model, modularity, randomizing networks, bi-fan, feed-forward loop, bi-parallel, feed-back loop, motif clusters, subgraph sampling
2018_Study_on_centrality_measures_in_social_networks_a_survey.pdf
1.2 MB
πStudy on centrality measures in social networks: a survey
πJournal: Social Network Analysis and Mining (I.F=3.868)
πPublish year: 2018
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #survey #centrality
πJournal: Social Network Analysis and Mining (I.F=3.868)
πPublish year: 2018
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #survey #centrality
2015_Network_analysis_for_a_network_disorder_The_emerging_role_of.pdf
1.7 MB
πNetwork analysis for a network disorder: The emerging role of graph theory in the study of epilepsy
πJournal:Epilepsy & Behavior (I.F=2.937)
πPublish year: 2015
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #epilepsy
πJournal:Epilepsy & Behavior (I.F=2.937)
πPublish year: 2015
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #epilepsy
π2
πGraph Theory in the Information Age
π₯This article is based on the Noether Lecture given at the
AMS-MAA-SIAM Annual Meeting, January 2009, Washington D. C.
πPublish year: 2010
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper
π₯This article is based on the Noether Lecture given at the
AMS-MAA-SIAM Annual Meeting, January 2009, Washington D. C.
πPublish year: 2010
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper
πStructure and tie strengths in mobile communication networks
πJournal: PNAS (I.F=11.205)
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #mobile_communication
πJournal: PNAS (I.F=11.205)
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #mobile_communication
πA Critical Review of Centrality Measures in Social Networks
πJournal: Business & Information Systems Engineering (I.F=4.532)
πPublish year: 2010
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #Review #Centrality
πJournal: Business & Information Systems Engineering (I.F=4.532)
πPublish year: 2010
πStudy paper
π²Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #Review #Centrality
π Machine Learning with Graphs: Choice of Graph Representation
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯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, weβll be looking at the different approaches to abstracting graphs: directed vs. undirected, weighted vs. unweighted, homogeneous vs bipartite, and so on.
π½ Watch
π Slides
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
π₯Free recorded course by Jure Leskovec, Computer Science, PhD
π₯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, weβll be looking at the different approaches to abstracting graphs: directed vs. undirected, weighted vs. unweighted, homogeneous vs bipartite, and so on.
π½ Watch
π Slides
π²Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.3 - Choice of Graph Representationβ
For more information about Stanfordβs Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3CmrFSE
Jure Leskovec
Computer Science, PhD
One essential task to consider before we conduct machine learning on graphs is to findβ¦
Jure Leskovec
Computer Science, PhD
One essential task to consider before we conduct machine learning on graphs is to findβ¦