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๐ŸŽž Machine Learning with Graphs: Generative Models for Graphs, Erdos Renyi Random Graphs, The Small World Model, Kronecker Graph Model


๐Ÿ’ฅFree recorded course by Jure Leskovec, Computer Science, PhD

๐Ÿ’ฅThis 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, Erdรถs-Renyi graph (E-R graphs, Gnp graphs). The small-world graphs (Wattsโ€“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.

๐Ÿ“ฝ Watch: part1 part2 part3 part4

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #Erdos_Renyi #Small_World #Kronecker_Graph
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๐ŸŽž Machine Learning with Graphs: Deep Generative Models for Graphs, Graph RNN: Generating Realistic Graphs, Scaling Up & Evaluating Graph Gen, Applications of Deep Graph Generation.


๐Ÿ’ฅFree recorded course by Jure Leskovec, Computer Science, PhD

๐Ÿ’ฅthis 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.

๐Ÿ“ฝ Watch: part1 part2 part3 part4

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #GCPN #GraphRNN #DGNN #GNN
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๐ŸŽž Machine Learning with Graphs: Generative Models for Graphs

๐Ÿ’ฅFree recorded course by Jure Leskovec, Computer Science, PhD

๐Ÿ’ฅIn 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. Graph generation is important as it can offer insight on the formulation process of graphs, which is crucial for predictions, simulations and anomaly detections on graphs. In the first part, we will introduce the properties of real-world graphs, where a successful graph generative model should fit these properties. These graph statistics include degree distribution, clustering coefficient, connected components and path length.

๐Ÿ“ฝ Watch

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #Generative_Models
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๐ŸŽž Graph Analytics and Graph-based Machine Learning

๐Ÿ’ฅFree recorded course by Clair Sullivan(Neo4j)

๐Ÿ’ฅMachine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data points. Network graphs provide great opportunities for identifying relationships that we may not even realize exist within our data. Further, a variety of methods exist to create embeddings of graphs that can enrich models and provide new insights.
In this talk we will look at some examples of common ML problems and demonstrate how they can take advantage of graph analytics and graph-based
machine learning. We will also demonstrate how graph embeddings can be used to enhance existing ML pipelines.

๐Ÿ“ฝ Watch

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning
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๐Ÿ“„Machine Learning for Refining Knowledge Graphs: A Survey

๐Ÿ“˜ Journal: acm digital library (I.F=14.324)
๐Ÿ—“Publish year: 2020

๐Ÿ“ŽStudy paper

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Knowledge_Graphs #Survey
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๐ŸŽž Machine Learning with Graphs: Graph Neural Networks in Computational Biology

๐Ÿ’ฅFree recorded course by Prof. Marinka Zitnik

๐Ÿ’ฅIn this lecture, Prof. Marinka gives an overview of why graph learning techniques can greatly help with computational biology research. Concretely, this talk covers 3 exemplar use cases: (1) Discovering safe drug-drug combinations via multi-relational link prediction on heterogenous knowledge graphs; (2) Classify patient outcomes and diseases via learning subgraph embeddings; and (3) Learning effective disease treatments through few-shot learning for graphs.

๐Ÿ“ฝ Watch

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis

#video #course #Graph #GNN #Machine_Learning #computational_biology
๐Ÿ‘4๐ŸŽ‰1
๐ŸŽž Machine Learning with Graphs: Pre-Training Graph Neural Networks

๐Ÿ’ฅFree recorded course by Prof. Jure Leskovec

๐Ÿ’ฅThere are two challenges in applying GNNs to scientific domains: scarcity of labeled data and out-of-distribution prediction. In this video we discuss methods for pre-training GNNs to resolve these challenges. The key idea is to pre-train both node and graph embeddings, which leads to significant performance gains on downstream tasks.

๐Ÿ“ฝ Watch
๐Ÿ“‘More details can be found in the paper: Strategies for Pre-training Graph Neural Networks

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning
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๐Ÿ“ƒ A Survey on Machine Learning Solutions for Graph Pattern Extraction

๐Ÿ—“ Publish year: 2022

๐Ÿง‘โ€๐Ÿ’ปAuthors:Kai Siong Yow, Ningyi Liao, Siqiang Luo, Reynold Cheng, Chenhao Ma, Xiaolin Han
๐ŸขUniversity: g, Nanyang Technological University
๐Ÿ—บ China, Singapore

๐Ÿ“Ž Study the paper

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
#paper #Survey #Machine_learning #Pattern
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๐ŸŽ“ Machine Learning for Graph Algorithms and Representations

๐Ÿ“˜A Thesis Submitted to the Faculty in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering Sciences
๐Ÿ—“Publish year: 2024

๐Ÿง‘โ€๐Ÿ’ปAuthor: Allison Mann
๐ŸขUniversity: College Hanover, New Hampshire

๐Ÿ“ŽStudy Thesis

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis
#Thesis #Machine_Learning #Algorithms #Representations
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๐Ÿ“ƒCommunity detection in social networks using machine learning: a systematic mapping study

๐Ÿ—“ Publish year: 2024
๐Ÿ“˜Journal: Knowledge and Information Systems (I.F=2.5)

๐Ÿง‘โ€๐Ÿ’ปAuthors: Mahsa Nooribakhsh, Marta Fernรกndez-Diego, Fernando Gonzรกlez-Ladrรณn-De-Guevara. Mahdi Mollamotalebi
๐ŸขUniversity: Universitat Politรจcnica de Valรจncia, Camino de Vera, s/n, 46022, Valencia, Spain and Islamic Azad University, Qazvin, Iran

๐Ÿ“Ž Study paper

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis
#paper #Community_detection #Machine_learning #mapping
๐ŸŽ“Graph Data Science and machine learning applications

๐Ÿ“•Master Degree Thesis by Antonella Cardillo form POLITECNICO DI TORINO

๐Ÿ—“Publish year: 2024

๐Ÿ“Ž Study thesis

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
#thesis #Graph #machine_learning #Data_Science
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๐ŸŽž Machine Learning with Graphs: hyperbolic graph embeddings

๐Ÿ’ฅFree recorded course by Prof. Jure Leskovec

๐Ÿ’ฅ This part focused on graph representation learning in Euclidean embedding spaces. In this lecture, we introduce hyperbolic embedding spaces, which are great for modeling hierarchical, tree-like graphs. Moreover, we introduce basics for hyperbolic geometry models, which leads to the idea of hyperbolic GNNs. More details can be found in the paper: Hyperbolic Graph Convolutional Neural Networks

๐Ÿ“ฝ Watch

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning
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