📄Wolfram MathWorld
💥Technical online booklet and workspace
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#online_book #Graph #Graph_Theory
💥Technical online booklet and workspace
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#online_book #Graph #Graph_Theory
👍3
📄New Developments in Social Network Analysis
📘 journal: Annual Review of Organizational Psychology and Organizational Behavior (I.F=13.7)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Developments
📘 journal: Annual Review of Organizational Psychology and Organizational Behavior (I.F=13.7)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Developments
👍3
🎞 Community Detection in R in 2021 and Beyond, Part 1
💥2021 Social Networks Workshop
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Community_Detection #R
💥2021 Social Networks Workshop
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Community_Detection #R
YouTube
Community Detection in R in 2021 and Beyond, Part 1
👍4
2020_Graph_weeds_net_A_graph_based_deep_learning_method_for_weed.pdf
2.7 MB
📄Graph weeds net: A graph-based deep learning method for weed recognition
📘 journal: Computers and Electronics in Agriculture (I.F=6.757)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #deep_learnin #weed_recognition
📘 journal: Computers and Electronics in Agriculture (I.F=6.757)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #deep_learnin #weed_recognition
👍3❤2
2021-Graphnet Graph Clustering with Deep Neural Networks.pdf
2.3 MB
📄Graphnet: Graph Clustering with Deep Neural Networks
📘 Conference: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graphnet #Deep_Neural_Networks #Clustering
📘 Conference: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graphnet #Deep_Neural_Networks #Clustering
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🎞 Anomaly Detection: Algorithms, Explanations, Applications
💥Free recorded tutorial by Dr. Dietterich’s.He is part of the leadership team for OSU’s Ecosystem Informatics programs including the NSF Summer Institute in Ecoinformatics
💥Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly “alarms” to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Anomaly_Detection #Algorithms #Explanations #Applications
💥Free recorded tutorial by Dr. Dietterich’s.He is part of the leadership team for OSU’s Ecosystem Informatics programs including the NSF Summer Institute in Ecoinformatics
💥Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly “alarms” to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Anomaly_Detection #Algorithms #Explanations #Applications
YouTube
Anomaly Detection: Algorithms, Explanations, Applications
Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly…
👍2
📄Current and future directions in network biology
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #biology
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #biology
❤2
🎞 Network data visualization in Gephi
💥Dr. Daria Maltseva, PhD, Head, International Laboratory for Applied Network Research, HSE.
💥14th Summer School 'Methods and Tools for Social Network Analysis'.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Network #data #visualization #Gephi
💥Dr. Daria Maltseva, PhD, Head, International Laboratory for Applied Network Research, HSE.
💥14th Summer School 'Methods and Tools for Social Network Analysis'.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Network #data #visualization #Gephi
YouTube
14th SS 2023. Day 2. Network data visualization in Gephi
Tamara Shcheglova, Doctoral student, Junior Research Fellow, Visiting Lecturer, International laboratory for Applied Network Research, HSE
👍1👏1
📄Graph Neural Networks in IoT: A Survey
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #IOT #survey
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #IOT #survey
👍2
📄Statistical Network Analysis: Past, Present, and Future
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Statistical_Network #Past #Present #Future
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Statistical_Network #Past #Present #Future
❤1👍1
🎞 Social Network Analysis | Chapter 4 | Link Analysis | Part 2
💥This is supplementary material for the book Social Network Analysis by Dr. Tanmoy Chakraborty.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Link_Analysis
💥This is supplementary material for the book Social Network Analysis by Dr. Tanmoy Chakraborty.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Link_Analysis
YouTube
Social Network Analysis | Chapter 4 | Link Analysis | Part 2
This is supplementary material for the book Social Network Analysis by Dr. Tanmoy Chakraborty.
Book Website: https://social-network-analysis.in/
Available for purchase at: https://www.amazon.in/Social-Network-Analysis-Tanmoy-Chakraborty/dp/9354247830
Book Website: https://social-network-analysis.in/
Available for purchase at: https://www.amazon.in/Social-Network-Analysis-Tanmoy-Chakraborty/dp/9354247830
❤1
📄A Gentle Introduction to Graph Neural Networks
💥Technical online article
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#online_book #Graph #GNN
💥Technical online article
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#online_book #Graph #GNN
Distill
A Gentle Introduction to Graph Neural Networks
What components are needed for building learning algorithms that leverage the structure and properties of graphs?
👍5
🎞 Machine Learning with Graphs: Generative Models for Graphs, Erdos Renyi Random Graphs, The Small World Model, Kronecker Graph Model
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥This lecture, will cover generative models for graphs. The goal of generative models for graphs is to generate synthetic graphs which are similar to given example graphs. The simplest model for graph generation, Erdös-Renyi graph (E-R graphs, Gnp graphs). The small-world graphs (Watts–Strogatz graphs, W-S graphs). Even though the E-R graphs can fit the average path length of real-world graphs, its clustering coefficient is much smaller than real-world graphs. The small-world model is proposed to generative realistic graphs with both low diameter and high clustering coefficient. Specifically, W-S graphs are generative by randomly rewring edges from regular lattic graphs. The Kronecker Graph model, where graphs are generated in a recursive manner. The key motivation is that real-world graphs often exhibit self-similarity, where the whole structure of the graph has the same shape as its parts. Kronecker graphs are generated by recursively doing Kronecker product over the initiator matrix, which is trained to fit the statistics of the input dataset. We further discuss fast Kronecker generator algorithms. Finally, we show that Kronecker graphs and real graphs are very close in many important graph statistics.
📽 Watch: part1 part2 part3 part4
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Erdos_Renyi #Small_World #Kronecker_Graph
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥This lecture, will cover generative models for graphs. The goal of generative models for graphs is to generate synthetic graphs which are similar to given example graphs. The simplest model for graph generation, Erdös-Renyi graph (E-R graphs, Gnp graphs). The small-world graphs (Watts–Strogatz graphs, W-S graphs). Even though the E-R graphs can fit the average path length of real-world graphs, its clustering coefficient is much smaller than real-world graphs. The small-world model is proposed to generative realistic graphs with both low diameter and high clustering coefficient. Specifically, W-S graphs are generative by randomly rewring edges from regular lattic graphs. The Kronecker Graph model, where graphs are generated in a recursive manner. The key motivation is that real-world graphs often exhibit self-similarity, where the whole structure of the graph has the same shape as its parts. Kronecker graphs are generated by recursively doing Kronecker product over the initiator matrix, which is trained to fit the statistics of the input dataset. We further discuss fast Kronecker generator algorithms. Finally, we show that Kronecker graphs and real graphs are very close in many important graph statistics.
📽 Watch: part1 part2 part3 part4
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Erdos_Renyi #Small_World #Kronecker_Graph
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 14.1 - Generative Models for Graphs
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jO8OsE
Jure Leskovec
Computer Science, PhD
In this lecture, we will cover generative models for graphs. The goal of generative…
Jure Leskovec
Computer Science, PhD
In this lecture, we will cover generative models for graphs. The goal of generative…
👍5
2022_A_review_of_challenges_and_solutions_in_the_design_and_implementation.pdf
2 MB
📄A review of challenges and solutions in the design and implementation of deep graph neural networks
📘 journal: Artificial Intelligence Review (I.F=0.381)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #review #GNN #implementation
📘 journal: Artificial Intelligence Review (I.F=0.381)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #review #GNN #implementation
👍2👏2
📄Everything is connected: Graph neural networks
📘 journal: Current Opinion in Structural Biology (I.F=6.8)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN
📘 journal: Current Opinion in Structural Biology (I.F=6.8)
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN
👍3👏1
📄Graph Representation Learning
📘 Journal: European Symposium on Artificial Neural Networks
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #representation
📘 Journal: European Symposium on Artificial Neural Networks
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #representation
👍2
📄Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence
📘 Journal: International journal of intelligent systems (IF=7)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GCN #overview
📘 Journal: International journal of intelligent systems (IF=7)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GCN #overview
👍3❤1
🎞 Machine Learning with Graphs: Deep Generative Models for Graphs, Graph RNN: Generating Realistic Graphs, Scaling Up & Evaluating Graph Gen, Applications of Deep Graph Generation.
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥this lecture, focus on deep generative models for graphs. We outline 2 types of tasks within the problem of graph generation: (1) realistic graph generation, where the goal is to generate graphs that are similar to a given set of graphs; (2) goal-directed graph generation, where we want to generate graphs that optimize given objectives/constraints. First, we recap the basics for generative models and deep generative models; then, in next parts introduce and focus on GraphRNN, one of the first deep generative models for graph; and finally, discuss GCPN, a deep graph generative model designed specifically for application to molecule generation.
📽 Watch: part1 part2 part3 part4
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #GCPN #GraphRNN #DGNN #GNN
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥this lecture, focus on deep generative models for graphs. We outline 2 types of tasks within the problem of graph generation: (1) realistic graph generation, where the goal is to generate graphs that are similar to a given set of graphs; (2) goal-directed graph generation, where we want to generate graphs that optimize given objectives/constraints. First, we recap the basics for generative models and deep generative models; then, in next parts introduce and focus on GraphRNN, one of the first deep generative models for graph; and finally, discuss GCPN, a deep graph generative model designed specifically for application to molecule generation.
📽 Watch: part1 part2 part3 part4
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #GCPN #GraphRNN #DGNN #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 15.1 - Deep Generative Models for Graphs
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Ex8TsH
Jure Leskovec
Computer Science, PhD
In this lecture, we focus on deep generative models for graphs. We outline 2 types of…
Jure Leskovec
Computer Science, PhD
In this lecture, we focus on deep generative models for graphs. We outline 2 types of…
👍3🎉1
2018-Structure-oriented prediction in complex networks.pdf
2.9 MB
📄Structure-oriented prediction in complex networks
📘 Journal: Physics Reports (IF=25.6)
🗓Publish year: 2018
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #prediction
📘 Journal: Physics Reports (IF=25.6)
🗓Publish year: 2018
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #prediction
👍4