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
π Introduction to Graph Computing
π₯Free recorded Lecture by Prof. Yadong Li
π₯Graph computing is an innovative technology that allows developers to build applications and systems as directed acyclic graphs (DAGs). Graph computing offers generic solutions to some of the most fundamental challenges in enterprise computing such as scalability, transparency and lineage. In this workshop, we survey the available graph computing tools in Julia, then walk through a few hands-on examples of building real world applications and systems using graph computing.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Lecture #GraphComputing
π₯Free recorded Lecture by Prof. Yadong Li
π₯Graph computing is an innovative technology that allows developers to build applications and systems as directed acyclic graphs (DAGs). Graph computing offers generic solutions to some of the most fundamental challenges in enterprise computing such as scalability, transparency and lineage. In this workshop, we survey the available graph computing tools in Julia, then walk through a few hands-on examples of building real world applications and systems using graph computing.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Lecture #GraphComputing
YouTube
Introduction to Graph Computing | JuliaCon 2022 | Yadong Li
Graph computing is an innovative technology that allows developers to build applications and systems as directed acyclic graphs (DAGs). Graph computing offers generic solutions to some of the most fundamental challenges in enterprise computing such as scalabilityβ¦
πDeep Learning for Community Detection: Progress, Challenges and Opportunities
πConference: Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}
πPublish year: 2020
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #CommunityDetection #DeepLearning
πConference: Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}
πPublish year: 2020
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #CommunityDetection #DeepLearning
πGraph neural networks: A review of methods and applications
πJournal: AI Open
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #review #applications
πJournal: AI Open
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #review #applications
πStatistical Network Analysis: A Review with Applications to the Coronavirus Disease 2019 Pandemic
πJournal: International Statistical Institute (I.F=1.946)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #review #Applications #Coronavirus
πJournal: International Statistical Institute (I.F=1.946)
πPublish year: 2020
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #review #Applications #Coronavirus
πA Review of Graph and Network Complexity from an Algorithmic Information Perspective
πJournal: Entropy (I.F=2.738)
πPublish year: 2018
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #review
πJournal: Entropy (I.F=2.738)
πPublish year: 2018
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #review
πBipartite Graphs as Models of Complex Networks
πPublish year: 2021
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper
πPublish year: 2021
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper
πCommunity Detection Algorithms
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #CommunityDetection
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #CommunityDetection
Medium
Community Detection Algorithms
Many of you are familiar with networks, right? You might be using social media sites such as Facebook, Instagram, Twitter, etc. They areβ¦
πA Review on Graph Theory in Network and Artificial Intelligence
πConference: International Conference on Robotics and Artificial Intelligence (RoAI) 2020 28-29 December 2020, Chennai, India
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #review #Artificial_Intelligence
πConference: International Conference on Robotics and Artificial Intelligence (RoAI) 2020 28-29 December 2020, Chennai, India
πPublish year: 2021
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #review #Artificial_Intelligence
π Modeling epidemics on complex networks
π₯Free recorded Lecture in Department of Computer Science IIL Ropar
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Lecture
π₯Free recorded Lecture in Department of Computer Science IIL Ropar
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Modeling epidemics on complex networks
πCommunity Detection Methods in Social Network Analysis
πJournal: Journal of Computational and Theoretical Nanoscience (I.F=0.488)
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #CommunityDetection
πJournal: Journal of Computational and Theoretical Nanoscience (I.F=0.488)
π Study the paper
π²Channel: @ComplexNetworkAnalysis
#paper #CommunityDetection
π Multi-agent models in complex networks
π₯Free recorded Lecture by Pablo Balenzuela (University of Buenos Aires, Argentina)
π½ Watch: part1 part2 part3 part4
π²Channel: @ComplexNetworkAnalysis
#video #Lecture
π₯Free recorded Lecture by Pablo Balenzuela (University of Buenos Aires, Argentina)
π½ Watch: part1 part2 part3 part4
π²Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Multi-agent models in complex networks (1 o 4)
Preparatory School for StatPhys 2019
July 1-5, 2019
Introduction to nonlinear dynamics
Speaker:
Pablo Balenzuela (University of Buenos Aires, Argentina)
More informations: https://www.ictp-saifr.org/preparatory-school-for-statphys-2019/
July 1-5, 2019
Introduction to nonlinear dynamics
Speaker:
Pablo Balenzuela (University of Buenos Aires, Argentina)
More informations: https://www.ictp-saifr.org/preparatory-school-for-statphys-2019/
π2
πImplement Louvain Community Detection Algorithm using Python and Gephi with visualization
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #CommunityDetection #Gephi #Louvain #code #python
π₯Technical paper
π Study
π²Channel: @ComplexNetworkAnalysis
#paper #CommunityDetection #Gephi #Louvain #code #python
Medium
Implement Louvain Community Detection Algorithm using Python and Gephi with visualization
Louvain Community Detection Algorithm
π1
π Introduction to Static Complex Networks
π₯Free recorded course by Professor Stephen Lansing
π₯This course explores the features of complexity science. Our world is connected by an abundance of complex systems. Across all levels of organizations from physical, biological world to the social world, we may think of the connectivity between individual elements and how they interact and influence each other. For example, how humans transmit pandemics within a group, how cars interact in the traffic system and how networks connect in governmental organizations. Although these systems are diverse and different, they have surprisingly huge features in common. In the past several decades, the study of complexity science has been increasing. It is widely acknowledged that an innovative, integrated and analytical way of thinking is essential for understanding the complex issues in the human societies. In this course, we will aim to give everyone a comprehensive introduction of the complex systems, to talk about the resilience, robustness and sustainability of the systems and to learn basic mathematical methods for complex system analysis, for example regime shifts and tipping points, the agent-based modelling, the dynamic and network theories. Most importantly, we will implement the theories into practical applications of cities and health to help students gain practice in complex systems way of thinking.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course
π₯Free recorded course by Professor Stephen Lansing
π₯This course explores the features of complexity science. Our world is connected by an abundance of complex systems. Across all levels of organizations from physical, biological world to the social world, we may think of the connectivity between individual elements and how they interact and influence each other. For example, how humans transmit pandemics within a group, how cars interact in the traffic system and how networks connect in governmental organizations. Although these systems are diverse and different, they have surprisingly huge features in common. In the past several decades, the study of complexity science has been increasing. It is widely acknowledged that an innovative, integrated and analytical way of thinking is essential for understanding the complex issues in the human societies. In this course, we will aim to give everyone a comprehensive introduction of the complex systems, to talk about the resilience, robustness and sustainability of the systems and to learn basic mathematical methods for complex system analysis, for example regime shifts and tipping points, the agent-based modelling, the dynamic and network theories. Most importantly, we will implement the theories into practical applications of cities and health to help students gain practice in complex systems way of thinking.
π½ Watch
π²Channel: @ComplexNetworkAnalysis
#video #course
Coursera
Introduction to Static Complex Networks (Part I) - Professor Stephen Lansing - Week 5: Introduction to Static Complex Network |β¦
Video created by Nanyang Technological University, ...
πGraph Neural Networks: a bibliometrics overview
πJournal: Machine Learning with Applications (MLWA)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #overview
πJournal: Machine Learning with Applications (MLWA)
πPublish year: 2022
πStudy paper
π±Channel: @ComplexNetworkAnalysis
#paper #overview