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📄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
📄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
🎞 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
💥In this lecture, we 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
📄A Gentle Introduction to Graph Neural Networks
💥Technical online article
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#online_book #Graph #GNN
🎞 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
📄Statistical Network Analysis: Past, Present, and Future
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Statistical_Network #Past #Present #Future
+2📄Graph Neural Networks in IoT: A Survey
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #IOT #survay
🎞 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
🎞 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
🎞 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
📄Current and future directions in network biology
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #biology
🎞 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
📄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
📄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
🎞 Community Detection in R in 2021 and Beyond, Part 1
💥2021 Social Networks Workshop
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Community_Detection #R
📄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
📄Wolfram MathWorld
💥Technical online booklet and workspace
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Grph_Learning
