اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
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آرشیو پست ها
📄Applications of social network analysis in promoting circular economy: a literature review
📘 Published by Vilnius Gediminas Technical University.
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #social_network #review #economy
📄A Review on Graph Neural Network Methods in Financial Applications
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Financial #Applications #review
🎞 pytorch geometric tutorial: graph attention networks implementation
💥Free recorded course
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GAT #code #python
📄GCN-tutorial
💥Technical paper
💥 Graph Convolutional Network. Perform convolution operations on a graph using the information embedded into each node. The main idea is to "look" at neighboor nodes and update the currently embedded information into a higher or lower dimensional space by performing a ReLU or softmax operation.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #python #GCN #Coda
📄Do we need deep graph neural networks?
💥Technical paper
💥 One of the hallmarks of deep learning was the use of neural networks with tens or even hundreds of layers. In stark contrast, most of the architectures used in graph deep learning are shallow with just a handful of layers. In this post, I raise a heretical question: does depth in graph neural network architectures bring any advantage?
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #DGNN
🎞 application of machine learning of traffic optimization
💥Free recorded course by Powel gora
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
📄Structure-oriented prediction in complex networks
📘 Journal: Physics Reports (IF=25.6)
🗓Publish year: 2018
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #prediction
🎞 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 #Erdos_Renyi #Small_World #Kronecker_Graph
📄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
📄Graph Representation Learning
📘 journal: European Symposium on Artificial Neural Networks
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN
📄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.
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📱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
