📄Nature‑inspired optimization algorithms for community detection in complex networks: a review and future trends
📘Journal: Telecommunication Systems(I.F=2.336)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #optimization_algorithms #community #trends #review
📘Journal: Telecommunication Systems(I.F=2.336)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #optimization_algorithms #community #trends #review
🎞 Machine learning and link prediction
💥Free recorded tutorial by Mark Needham & Jennifer Reif
💥In this session, will show what graph has to offer and show an example applying link prediction analysis to estimate how likely academic authors are to collaborate with new co-authors in the future
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📱Channel: @ComplexNetworkAnalysis
#video #Machine_learning
💥Free recorded tutorial by Mark Needham & Jennifer Reif
💥In this session, will show what graph has to offer and show an example applying link prediction analysis to estimate how likely academic authors are to collaborate with new co-authors in the future
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Machine_learning
YouTube
Machine learning and link prediction by Mark Needham & Jennifer Reif
Machine learning uses algorithms to train software through specific examples and progressive improvements based on expected outcome. However, traditional data structures can fail to detect behavior without the contextual information because they lack the…
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2021_New_research_methods_&_algorithms_in_social_network_analysis.pdf
525.4 KB
📄New research methods & algorithms in social network analysis
📘Journal: Future Generation Computer Systems (I.F=8.872 )
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_network
📘Journal: Future Generation Computer Systems (I.F=8.872 )
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_network
2020-Finding key players in complex networks through.pdf
2.4 MB
📄Finding key players in complex networks through deep reinforcement learning
📘Journal: Nature Machine Intelligence (I.F=25.9)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #deep_reinforcement_learning
📘Journal: Nature Machine Intelligence (I.F=25.9)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #deep_reinforcement_learning
📄deep learning for Complex Networks
💥research paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #deep_Learning
💥research paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #deep_Learning
TU Delft
Deep learning for complex networks
📄Complex Networks and Machine Learning: From Molecular to Social Sciences
📘Journal: applied science (I.F=2.679)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
📘Journal: applied science (I.F=2.679)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
2015_Estimating_Complex_Networks_Centrality_via_neural_networks.pdf
1 MB
📄Estimating Complex Networks Centrality via neural networks and machine learning
📘Conference : 2015 International Joint Conference on Neural Networks (IJCNN)
🗓Publish year: 2015
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
📘Conference : 2015 International Joint Conference on Neural Networks (IJCNN)
🗓Publish year: 2015
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
🎞 Lecture12. Link Prediction
💥Free recorded Lecture on Link Prediction
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Link_Prediction
💥Free recorded Lecture on Link Prediction
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Link_Prediction
YouTube
Lecture12. Link Prediction
Network Science 2021 @ HSE
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📄A survey of data mining and social network analysis based anomaly detection techniques
📘Journal: EGYPTIAN INFORMATICS JOURNAL (I.F= 4.195)
🗓Publish year: 2016
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #data_mining #anomaly_detection #survey
📘Journal: EGYPTIAN INFORMATICS JOURNAL (I.F= 4.195)
🗓Publish year: 2016
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #data_mining #anomaly_detection #survey
🎞 Course "Social Network Analysis". Lecture 1. Terminology
💥Free recorded course by Leonid Zhukov
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Terminology
💥Free recorded course by Leonid Zhukov
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Terminology
YouTube
Course "Social Network Analysis" (Leonid Zhukov). Lecture 1. Terminology
Course outline:
- Introduction to network science
- Denoscriptive network analysis
- Mathematical models of networks
- Node centrality and ranking on networks
- Network communities
- Network structure and visualization
- Social media and information flow in…
- Introduction to network science
- Denoscriptive network analysis
- Mathematical models of networks
- Node centrality and ranking on networks
- Network communities
- Network structure and visualization
- Social media and information flow in…
2016-Machine Learning in Complex Networks (1).pdf
8.5 MB
📘 Machine Learning in Complex Networks
📝Authors: Thiago Christiano Silva, Liang Zhao
📅Publish year: 2016
💥This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning.
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#book #Machine_Learning
📝Authors: Thiago Christiano Silva, Liang Zhao
📅Publish year: 2016
💥This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning.
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#book #Machine_Learning
👍3
📄A survey on text mining in social networks
📘Journal: KNOWLEDGE ENGINEERING REVIEW (I.F= 2.016)
🗓Publish year: 2015
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #text_mining #survey
📘Journal: KNOWLEDGE ENGINEERING REVIEW (I.F= 2.016)
🗓Publish year: 2015
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #text_mining #survey
📄Challenges and Limitations of Biological Network Analysis
📘Journal: BioTech
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Biological
📘Journal: BioTech
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Biological
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📄A survey on hierarchical community detection in large-scale complex networks
📘Journal: AUT Journal of Mathematics and Computing
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #community #large_scale #survey
📘Journal: AUT Journal of Mathematics and Computing
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #community #large_scale #survey
🎞 Machine Learning with Graphs
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥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.
📽 Watch
📜 Slides
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥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.
📽 Watch
📜 Slides
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
👍2
📄Consensus clustering in complex networks
📘Journal: Scientific Reports(I.F=5.516)
🗓Publish year: 2012
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Consensus_clustering
📘Journal: Scientific Reports(I.F=5.516)
🗓Publish year: 2012
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Consensus_clustering
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📄Network analysis approach to Likert-style surveys
📘Journal: PHYSICAL REVIEW PHYSICS EDUCATION RESEARCH (I.F=2.359)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Likert_style #survey
📘Journal: PHYSICAL REVIEW PHYSICS EDUCATION RESEARCH (I.F=2.359)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Likert_style #survey
📄Motif discovery algorithms in static and temporal networks: A survey
📘Journal: Journal of Complex Networks(I.F=2.011)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Motif #survey
📘Journal: Journal of Complex Networks(I.F=2.011)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Motif #survey
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🎞 Closeness Centrality & Betweenness Centrality: A Social Network Lab in R for Beginners
💥Free recorded course
💥So what then is “closeness” or “betweenness” 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.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Closeness_Centrality #Betweenness_Centrality #code #R
💥Free recorded course
💥So what then is “closeness” or “betweenness” 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.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Closeness_Centrality #Betweenness_Centrality #code #R
YouTube
Closeness Centrality & Betweenness Centrality: A Social Network Lab in R for Beginners
DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ
So what then is “closeness” or “betweenness” in a network? How do we figure these things out and how do we interpret them? This video is part of a series where…
So what then is “closeness” or “betweenness” in a network? How do we figure these things out and how do we interpret them? This video is part of a series where…
📄Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale
📘Journal: PLOS ONE(I.F=3.752)
🗓Publish year: 2016
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Clustering
📘Journal: PLOS ONE(I.F=3.752)
🗓Publish year: 2016
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Clustering
📄A survey of game theory as applied to social networks
📘Journal: T singhua Science and Technology (I.F=3.515)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #game_theory #survey
📘Journal: T singhua Science and Technology (I.F=3.515)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #game_theory #survey
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