🎞 Network models
💥Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.
💥Barabasi-Albert model. Preferential attachement. Time evolition of node degrees. Node degree distribution. Average path length and clustering coefficient. Small world model. Watts-Strogats model. Transition from ragular to random. Clustering coefficient and ave path lenght.
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📑 Lecture
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.
💥Barabasi-Albert model. Preferential attachement. Time evolition of node degrees. Node degree distribution. Average path length and clustering coefficient. Small world model. Watts-Strogats model. Transition from ragular to random. Clustering coefficient and ave path lenght.
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📑 Lecture
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Lecture 4. Network models.
Network Science 2021 @ HSE
http://www.leonidzhukov.net/hse/2021/networks/
http://www.leonidzhukov.net/hse/2021/networks/
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🎞 Statistical analysis of networks
💥Free recorded Lecture by Prof. Gesine Reinert in University of Oxford.
💥Networks have become increasingly popular as representations of complex data. How can we make sense of such data? The first class will cover some network summaries and some parametric models for networks, while the second class concerns statistical inference using these network summaries as well as parametric models. We shall also cover some nonparametric ideas.
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📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture by Prof. Gesine Reinert in University of Oxford.
💥Networks have become increasingly popular as representations of complex data. How can we make sense of such data? The first class will cover some network summaries and some parametric models for networks, while the second class concerns statistical inference using these network summaries as well as parametric models. We shall also cover some nonparametric ideas.
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📲Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Statistical analysis of networks - Professor Gesine Reinert, University of Oxford
Networks have become increasingly popular as representations of complex data. How can we make sense of such data? The first class will cover some network summaries and some parametric models for networks, while the second class concerns statistical inference…
🎞 Network-Based Modeling of Complex Systems
💥Free recorded Lecture by Dr. Fatena El-Masri from QuantCon 2018
💥This talk, titled Network-based Modeling of Complex Systems, with Applications to Cascading and Contagion Events in Networks, looks at how network-based modeling can be used to analyze cascading and contagious events in financial networks. Dr. El-Masri’s research started with the simulation of 63 million records to determine the network and dynamic connectivity between different banks. This connectivity is determined by transforming the adjacency matrix for the data using Python. The agent-based model is then applied using dynamic modeling and validated using real data. The results are then implemented using network-based modeling for complex financial institutions to discover the cascading and contagious event behavior in the networks.
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📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture by Dr. Fatena El-Masri from QuantCon 2018
💥This talk, titled Network-based Modeling of Complex Systems, with Applications to Cascading and Contagion Events in Networks, looks at how network-based modeling can be used to analyze cascading and contagious events in financial networks. Dr. El-Masri’s research started with the simulation of 63 million records to determine the network and dynamic connectivity between different banks. This connectivity is determined by transforming the adjacency matrix for the data using Python. The agent-based model is then applied using dynamic modeling and validated using real data. The results are then implemented using network-based modeling for complex financial institutions to discover the cascading and contagious event behavior in the networks.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Network-Based Modeling of Complex Systems by Dr. Fatena El-Masri from QuantCon 2018
This talk, titled Network-based Modeling of Complex Systems, with Applications to Cascading and Contagion Events in Networks, looks at how network-based modeling can be used to analyze cascading and contagious events in financial networks. Dr. El-Masri’s…
🎞 Node centrality and ranking on networks
💥Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.
💥Node centrality metrics, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. Katz status index. Directed graphs. PageRank, Perron-Frobenius theorem and algorithm convergence. Power iterations. Hubs and Authorites. HITS algorithm.
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📑 Lecture
📲Channel: @ComplexNetworkAnalysis
#video #Lecture #centrality
💥Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.
💥Node centrality metrics, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. Katz status index. Directed graphs. PageRank, Perron-Frobenius theorem and algorithm convergence. Power iterations. Hubs and Authorites. HITS algorithm.
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📑 Lecture
📲Channel: @ComplexNetworkAnalysis
#video #Lecture #centrality
YouTube
Lecture 5. Node centrality and ranking on networks.
Network Science 2021 @ HSE
http://www.leonidzhukov.net/hse/2021/networks/
http://www.leonidzhukov.net/hse/2021/networks/
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🎞 Structural properties of networks
💥Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.
💥Structural and regular equivalence. Similarity metrics. Correlation coefficient and cosine similarity. Assortative mixing and homophily. Modularity. Assortativity coefficient. Mixing by node degree. Assortative and disassortative networks. Cohesive subgroups. Graph cliques. k-cores decomposition.
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📑 Lecture
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.
💥Structural and regular equivalence. Similarity metrics. Correlation coefficient and cosine similarity. Assortative mixing and homophily. Modularity. Assortativity coefficient. Mixing by node degree. Assortative and disassortative networks. Cohesive subgroups. Graph cliques. k-cores decomposition.
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📑 Lecture
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Lecture 6. Structural properties of networks
Network Science 2021 @ HSE
http://www.leonidzhukov.net/hse/2021/networks/
http://www.leonidzhukov.net/hse/2021/networks/
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🎞 Introduction to Brain Network Analysis
💥Free recorded Lecture by Dr. Johann D. Kruschwitz.
💥Graph Theoretical Modelling of Brain Connectivity.
📽 Watch: part1 part2
📲Channel: @ComplexNetworkAnalysis
#video #Lecture #Brain
💥Free recorded Lecture by Dr. Johann D. Kruschwitz.
💥Graph Theoretical Modelling of Brain Connectivity.
📽 Watch: part1 part2
📲Channel: @ComplexNetworkAnalysis
#video #Lecture #Brain
YouTube
Introduction to Brain Network Analysis - Part 1/2.
Introduction to Brain Network Analysis - Part 1/2. Graph Theoretical Modelling of Brain Connectivity. Concepts and Workflow. GraphVar by Dr. Johann D. Kruschwitz.
🎞 Graph partitioning.
💥Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.
💥Graph density. Graph pertitioning. Min cut, ratio cut, normalized and quotient cuts metrics. Spectral graph partitioning (normalized cut). Direct (spectral) modularity maximization. Multilevel recursive partitioning
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📑 Lecture
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.
💥Graph density. Graph pertitioning. Min cut, ratio cut, normalized and quotient cuts metrics. Spectral graph partitioning (normalized cut). Direct (spectral) modularity maximization. Multilevel recursive partitioning
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📑 Lecture
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Lecture 7. Graph partitioning algorithms.
Network Science 2021 @ HSE
http://www.leonidzhukov.net/hse/2021/networks/
http://www.leonidzhukov.net/hse/2021/networks/
🎞 Network communities.
💥Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.
💥Network communities. Edge Betweenness. Newman-Girvin algorithm. Community detection algorithms. Overlapping communities. Clique percolation method. Heuristic methods. Fast community unfolding. Random walk based methods. Walktrap.
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📑 Lecture
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.
💥Network communities. Edge Betweenness. Newman-Girvin algorithm. Community detection algorithms. Overlapping communities. Clique percolation method. Heuristic methods. Fast community unfolding. Random walk based methods. Walktrap.
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📑 Lecture
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Lecture8. Community detection
Network Science 2021 @ HSE
🎞 Epidemics on networks
💥Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.
💥Spread of epidemics on network. SI, SIS, SIR network models. Epidemic threshold. Simulations of infection propagation on networks
📽 Watch: part1 part2
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture by Prof. Leonid Zhukov, Ilya Makarov.
💥Spread of epidemics on network. SI, SIS, SIR network models. Epidemic threshold. Simulations of infection propagation on networks
📽 Watch: part1 part2
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Lecture9. Epidemics on networks I
Network Science 2021 @ HSE
🎞 Graph Mining
💥Free recorded Lecture
💥Graph Mining is the set of tools and techniques used to analyze the properties of real-world graphs, predict how the structure and properties of a given graph might affect some application, develop models that can generate realistic graphs that match the patterns found in real-world graphs of interest etc
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📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture
💥Graph Mining is the set of tools and techniques used to analyze the properties of real-world graphs, predict how the structure and properties of a given graph might affect some application, develop models that can generate realistic graphs that match the patterns found in real-world graphs of interest etc
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📲Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Graph Mining
Graph Mining, Used Applications, Methods for finding frequent sub graph and Apriori based approach are included.
Graph Mining is the set of tools and techniques used to analyze the properties of real-world graphs, predict how the structure and properties…
Graph Mining is the set of tools and techniques used to analyze the properties of real-world graphs, predict how the structure and properties…
🎞 Using NetLogo: Complex Problem Solving in Networks
💥Free recorded Lecture
💥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 –from 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 “invisible” 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
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📲Channel: @ComplexNetworkAnalysis
#video #Lecture #NetLogo
💥Free recorded Lecture
💥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 –from 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 “invisible” 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
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📲Channel: @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 ...
🎞 Introduction to Graph Computing
💥Free recorded Lecture by Prof. 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 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.
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📲Channel: @ComplexNetworkAnalysis
#video #Lecture #GraphComputing
💥Free recorded Lecture by Prof. 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 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.
📽 Watch
📲Channel: @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 scalability…
🎞 Modeling epidemics on complex networks
💥Free recorded Lecture in Department of Computer Science IIL Ropar
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture in Department of Computer Science IIL Ropar
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Modeling epidemics on complex networks
🎞 Multi-agent models in complex networks
💥Free recorded Lecture by Pablo Balenzuela (University of Buenos Aires, Argentina)
📽 Watch: part1 part2 part3 part4
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture by Pablo Balenzuela (University of Buenos Aires, Argentina)
📽 Watch: part1 part2 part3 part4
📲Channel: @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/
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🎞 Complex Networks, Simple Rules
💥Free recorded Lecture
💥Complex networks are all around us, and they can be generated by simple mechanisms. Understanding what kinds of networks can be produced by following simple rules is therefore of great importance. We investigate this issue by studying the dynamics of extremely simple systems where are `writer' moves around a network, and modifies it in a way that depends upon the writer's surroundings. Each vertex in the network has three edges incident upon it, which are colored red, blue and green.
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📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture
💥Complex networks are all around us, and they can be generated by simple mechanisms. Understanding what kinds of networks can be produced by following simple rules is therefore of great importance. We investigate this issue by studying the dynamics of extremely simple systems where are `writer' moves around a network, and modifies it in a way that depends upon the writer's surroundings. Each vertex in the network has three edges incident upon it, which are colored red, blue and green.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
🎞 Complex networks of time-series: what does it reveal more than local interactions?
💥Free recorded Lecture by Amirhossein Shirazi, IFISC (UIB-CSIC)
💥There is a huge literature about extracting the interaction network of these systems in molecular biology, neuroscience and economy. Although this approach invigorates these disciplines to deal with large data, it usually focuses on microscopic results. In this presentation, I will suggest some holistic approaches towards the analysis of these networks, based on two examples: medical words network evolution and stock market network near the crisis. Finally, I will try to connect the measured global indicators to dynamics of the system, using the idea of symmetry breaking in the spin glass models.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture by Amirhossein Shirazi, IFISC (UIB-CSIC)
💥There is a huge literature about extracting the interaction network of these systems in molecular biology, neuroscience and economy. Although this approach invigorates these disciplines to deal with large data, it usually focuses on microscopic results. In this presentation, I will suggest some holistic approaches towards the analysis of these networks, based on two examples: medical words network evolution and stock market network near the crisis. Finally, I will try to connect the measured global indicators to dynamics of the system, using the idea of symmetry breaking in the spin glass models.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Complex networks of time-series: what does it reveal more than local interactions?
- By: Amirhossein Shirazi, IFISC (UIB-CSIC)
- Date: 2015-09-24 14:30:00
- Description: In many complex systems, the only observable variables are the time-series and event-series of their components. There is a huge literature about extracting the interaction…
- Date: 2015-09-24 14:30:00
- Description: In many complex systems, the only observable variables are the time-series and event-series of their components. There is a huge literature about extracting the interaction…
🎞 The Structure of Complex Networks: Scale-Free and Small-World Random Graphs
💥Free recorded Lecture by Remco van der Hofstad
💥In this lecture for a broad audience, we describe a few real-world networks and some of their empirical properties. We also describe the effectiveness of abstract network modeling in terms of graphs and how real-world networks can be modeled, as well as how these models help us to give sense to the empirical findings. We continue by discussing some random graph models for real-world networks and their properties, as well as their merits and flaws as network models. We conclude by discussing the implications of some of the empirical findings on information diffusion and competition on such networks.
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📲Channel: @ComplexNetworkAnalysis
#video #Lecture
💥Free recorded Lecture by Remco van der Hofstad
💥In this lecture for a broad audience, we describe a few real-world networks and some of their empirical properties. We also describe the effectiveness of abstract network modeling in terms of graphs and how real-world networks can be modeled, as well as how these models help us to give sense to the empirical findings. We continue by discussing some random graph models for real-world networks and their properties, as well as their merits and flaws as network models. We conclude by discussing the implications of some of the empirical findings on information diffusion and competition on such networks.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Lecture
YouTube
Remco van der Hofstad - The Structure of Complex Networks: Scale-Free and Small-World Random Graphs
Abstract:
Many phenomena in the real world can be phrased in terms of networks. Examples include the World-Wide Web, social interactions and Internet, but also the interaction patterns between proteins, food webs and citation networks.
Many large-scale networks…
Many phenomena in the real world can be phrased in terms of networks. Examples include the World-Wide Web, social interactions and Internet, but also the interaction patterns between proteins, food webs and citation networks.
Many large-scale networks…
🎞 Graph-Powered Machine Learning
💥Free recorded Lecture
💥Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.
Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.
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📲Channel: @ComplexNetworkAnalysis
#video #Lecture #Machine_Learning
💥Free recorded Lecture
💥Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.
Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Lecture #Machine_Learning
YouTube
Graph-Powered Machine Learning
Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds…
🎞 Network Analysis
💥Free recorded lectures
🔹(1) Theory and Concept
🔹(2) Practice Using igraph and Gephi
📽 Watch: part1 part2
📲Channel: @ComplexNetworkAnalysis
#video #lecture #Gephi #igraph
💥Free recorded lectures
🔹(1) Theory and Concept
🔹(2) Practice Using igraph and Gephi
📽 Watch: part1 part2
📲Channel: @ComplexNetworkAnalysis
#video #lecture #Gephi #igraph
YouTube
Network Analysis (1) Theory and Concept
This video is for the Network analysis and visualization workshop organized at the Virtual Annual Conference of Comparative and International Education Society 2020 (vCIES 2020) in April 2020.
This lecture is designed for beginners to learn from concept…
This lecture is designed for beginners to learn from concept…
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