🎞 Machine Learning with Graphs: Theory of Graph Neural Networks
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥The topics: How expensive are graph neural networks, designing the most powerful GNNs.
📽 Watch: part1 part2
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #GNN
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥The topics: How expensive are graph neural networks, designing the most powerful GNNs.
📽 Watch: part1 part2
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 9.1 - How Expressive are Graph Neural Networks
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GwTmur
Jure Leskovec
Computer Science, PhD
In this lecture, we provide a theoretical framework to analyze the expressive power…
Jure Leskovec
Computer Science, PhD
In this lecture, we provide a theoretical framework to analyze the expressive power…
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🎞 Network Analysis of Organizations
💥Free recorded course by professor Daniel A. McFarland.
💥In this course, we will describe how organization’s researchers look at social networks within organizations. In addition, we will describe how some theorists contend there is a network form of organization that is distinct from hierarchical organizations and markets. So we will relate two perspectives: a purely analytic one that describes networks within organizations, and a theoretical one concerning a prescribed form of inter- organizational association that can result in better outputs.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #network
💥Free recorded course by professor Daniel A. McFarland.
💥In this course, we will describe how organization’s researchers look at social networks within organizations. In addition, we will describe how some theorists contend there is a network form of organization that is distinct from hierarchical organizations and markets. So we will relate two perspectives: a purely analytic one that describes networks within organizations, and a theoretical one concerning a prescribed form of inter- organizational association that can result in better outputs.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #network
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🎞 Network Theory
💥Free recorded course.
💥This lecture will discuss Network Theory:
Part I – Static networks:
🔸Understand the notion of networks as graphs consisting of nodes and edges:
-Directed vs undirected
-Weighted unweighted
🔸Understand different topologies and how they affect the network:
-Random
-Preferential
🔸Know the meaning of the basic network metrics:
-Graph diameter
-Shortest Average Path Length
-Degree distributions
-Minimum spanning tree
🔸Understand basic network evolution processes:
-Small world networks
Part II – Dynamic networks
Network visualization:
-Why network views are important
-Graph layouts
🔸Networks vs hierarchies
Using networks:
-Inuput/Output analysis
-LCA
🔸Measuring real networks:
-Economies
-Wikis/knowledge
-Ecosystems
🔸Processes on networks:
-Avalanche models
-Metcalfe’s law
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #network #Graph
💥Free recorded course.
💥This lecture will discuss Network Theory:
Part I – Static networks:
🔸Understand the notion of networks as graphs consisting of nodes and edges:
-Directed vs undirected
-Weighted unweighted
🔸Understand different topologies and how they affect the network:
-Random
-Preferential
🔸Know the meaning of the basic network metrics:
-Graph diameter
-Shortest Average Path Length
-Degree distributions
-Minimum spanning tree
🔸Understand basic network evolution processes:
-Small world networks
Part II – Dynamic networks
Network visualization:
-Why network views are important
-Graph layouts
🔸Networks vs hierarchies
Using networks:
-Inuput/Output analysis
-LCA
🔸Measuring real networks:
-Economies
-Wikis/knowledge
-Ecosystems
🔸Processes on networks:
-Avalanche models
-Metcalfe’s law
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #network #Graph
TU Delft OCW
Network Theory - TU Delft OCW
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🎞 Machine Learning with Graphs: Heterogeneous & Knowledge Graph Embedding, Knowledge Graph Completion
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 In this lecture, we first introduce the heterogeneous graph with the definition and several examples. In the next, we talk about a model called RGCN which extends the GCN to heterogeneous graph. To make the model more scalable, several approximated approaches are introduced, including block diagonal matrices and basis learning. At last, we show how RGCN predicts the labels of nodes and links.
Then we introduce the knowledge graphs by giving several examples and applications.
📽 Watch: part1 part2
📝 slide
💻 Code
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph #GNN
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 In this lecture, we first introduce the heterogeneous graph with the definition and several examples. In the next, we talk about a model called RGCN which extends the GCN to heterogeneous graph. To make the model more scalable, several approximated approaches are introduced, including block diagonal matrices and basis learning. At last, we show how RGCN predicts the labels of nodes and links.
Then we introduce the knowledge graphs by giving several examples and applications.
📽 Watch: part1 part2
📝 slide
💻 Code
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 10.1-Heterogeneous & Knowledge Graph Embedding
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pNkBLE
Lecture 10.1 - Heterogeneous Graphs and Knowledge Graph Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture,…
Lecture 10.1 - Heterogeneous Graphs and Knowledge Graph Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture,…
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🎞 Machine Learning with Graphs: Reasoning in Knowledge Graphs, Answering Predictive Queries, Query2box: Reasoning over KGs
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 IIn this lecture, we introduce how to perform reasoning over knowledge graphs and provide answers to complex queries. We talk about different possible queries that one can get over a knowledge graph, and how to answer them by traversing over the graph. We also show how incompleteness of knowledge graphs can limit our ability to provide complete answers. We finally talk about how we can solve this problem by generalizing the link prediction task.
📽 Watch: part1 part2 part3
📝 slide
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 IIn this lecture, we introduce how to perform reasoning over knowledge graphs and provide answers to complex queries. We talk about different possible queries that one can get over a knowledge graph, and how to answer them by traversing over the graph. We also show how incompleteness of knowledge graphs can limit our ability to provide complete answers. We finally talk about how we can solve this problem by generalizing the link prediction task.
📽 Watch: part1 part2 part3
📝 slide
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 11.1 - Reasoning in Knowledge Graphs
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3BweHQZ
Lecture 11.1 - Reasoning in Knowledge Graphs using Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture, we introduce…
Lecture 11.1 - Reasoning in Knowledge Graphs using Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture, we introduce…
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🎞 Machine Learning with Graphs: Neural Subgraph Matching & Counting, Neural Subgraph Matching, Finding Frequent Subgraphs
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, we will be talking about the problem on subgraph matching and counting. Subgraphs work as building blocks for larger networks, and have the power to characterize and discriminate networks. We first give an introduction on two types of subgraphs - node-induced subgraphs and edge-induced subgraphs. Then we give you an idea how to determine subgraph relation through the concept of graph isomorphism. Finally, we discuss why subgraphs are important, and how we can identify the most informative subgraphs with network significance profile.
📽 Watch: part1 part2 part3
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Subgraph
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, we will be talking about the problem on subgraph matching and counting. Subgraphs work as building blocks for larger networks, and have the power to characterize and discriminate networks. We first give an introduction on two types of subgraphs - node-induced subgraphs and edge-induced subgraphs. Then we give you an idea how to determine subgraph relation through the concept of graph isomorphism. Finally, we discuss why subgraphs are important, and how we can identify the most informative subgraphs with network significance profile.
📽 Watch: part1 part2 part3
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Subgraph
YouTube
CS224W: Machine Learning with Graphs | 2021 | Lecture 12.1-Fast Neural Subgraph Matching & Counting
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jR7jK2
Jure Leskovec
Computer Science, PhD
In this lecture, we will be talking about the problem on subgraph matching and counting.…
Jure Leskovec
Computer Science, PhD
In this lecture, we will be talking about the problem on subgraph matching and counting.…
🎞 Graph Analytics and Graph-based Machine Learning
💥Free recorded course by Clair Sullivan
💥Machine 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.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
💥Free recorded course by Clair Sullivan
💥Machine 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.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Graph Analytics and Graph-based Machine Learning
Machine 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…
🎞 Machine Learning with Graphs: Community Detection in Network, Network Communities, Louvain Algorithm, Detecting Overlapping Communities
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, introduce methods that build on the intuitions presented in the previous part to identify clusters within networks. We define modularity score Q that measures how well a network is partitioned into communities. We also introduce null models to measure expected number of edges between nodes to compute the score. Using this idea, we then give a mathematical expression to calculate the modularity score. Finally, we can develop an algorithm to find communities by maximizing the modularity..
📽 Watch: part1 part2 part3 part4
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Community_Detection
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, introduce methods that build on the intuitions presented in the previous part to identify clusters within networks. We define modularity score Q that measures how well a network is partitioned into communities. We also introduce null models to measure expected number of edges between nodes to compute the score. Using this idea, we then give a mathematical expression to calculate the modularity score. Finally, we can develop an algorithm to find communities by maximizing the modularity..
📽 Watch: part1 part2 part3 part4
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Community_Detection
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 13.1 - Community Detection in Networks
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Eu4Xss
Jure Leskovec
Computer Science, PhD
In this lecture, we first introduce the community structure of graphs and information…
Jure Leskovec
Computer Science, PhD
In this lecture, we first introduce the community structure of graphs and information…
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🎞 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
🎞 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…
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