🎞 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…
👍5
📄Graph Data Structure And Algorithms
💥Technical paper
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#paper #Graph #Data_Structure #Algorithms
💥Technical paper
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#paper #Graph #Data_Structure #Algorithms
GeeksforGeeks
Graph Algorithms - GeeksforGeeks
Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more.
👍5
📄Graph Theory
🧑🏻💼 author : Marc Lackenby
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📲Channel: @ComplexNetworkAnalysis
#paper #graph
🧑🏻💼 author : Marc Lackenby
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📲Channel: @ComplexNetworkAnalysis
#paper #graph
👍2
📄Graph Convolutional Networks: Introduction to GNNs
💥Technical paper
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#paper #Graph #GNN
💥Technical paper
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Medium
Graph Convolutional Networks: Introduction to GNNs
A step-by-step guide using PyTorch Geometric
❤2👍1
📄Community Detection Algorithms in Healthcare
Applications: A Systematic Review
📘 journal: IEEE Access (I.F=3.9)
🗓Publish year: 2023
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📱Channel: @ComplexNetworkAnalysis
#paper #Community_Detection #Healthcare #Applications #review
Applications: A Systematic Review
📘 journal: IEEE Access (I.F=3.9)
🗓Publish year: 2023
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📱Channel: @ComplexNetworkAnalysis
#paper #Community_Detection #Healthcare #Applications #review
👍3
📄The Use of Graph Theory for Modeling and Analyzing the Structure of a Complex System, with the Example of an Industrial Grain Drying Line
📘 journal: processes (I.F=3.352)
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #graph #Analysis #Industrial_Grain_Drying_Line
📘 journal: processes (I.F=3.352)
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #graph #Analysis #Industrial_Grain_Drying_Line
👍3
2023 -A comprehensive survey of personal knowledge graphs.pdf
2.2 MB
📄 A comprehensive survey of personal knowledge graphs
📘 journal: Data Mining and Knowledge Discovery (I.F=7.8)
🗓Publish year: 2023
📲Channel: @ComplexNetworkAnalysis
#paper #survey #knowledge_graphs
📘 journal: Data Mining and Knowledge Discovery (I.F=7.8)
🗓Publish year: 2023
📲Channel: @ComplexNetworkAnalysis
#paper #survey #knowledge_graphs
👍2
📄Influence maximization in social networks: a survey of behaviour-aware methods
📘 journal: Social Network Analysis and Mining (SNAM) (I.F=2.8)
🗓Publish year: 2023
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📱Channel: @ComplexNetworkAnalysis
#paper #Influence #maximization #behaviour_aware #survey
📘 journal: Social Network Analysis and Mining (SNAM) (I.F=2.8)
🗓Publish year: 2023
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📱Channel: @ComplexNetworkAnalysis
#paper #Influence #maximization #behaviour_aware #survey
👍1
📄Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
🗓Publish year: 2023
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📱Channel: @ComplexNetworkAnalysis
#paper #Privacy #Preserving #Graph_Machine_Learning #Computation #survey
🗓Publish year: 2023
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#paper #Privacy #Preserving #Graph_Machine_Learning #Computation #survey
👍3
📄 A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
🗓Publish year: 2023
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📲Channel: @ComplexNetworkAnalysis
#paper #survey #GNN #anomaly_detection #time_series
🗓Publish year: 2023
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#paper #survey #GNN #anomaly_detection #time_series
👍5
📄A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges
📘 journal: ACM Computing Surveys (I.F=16.6)
🗓Publish year: 2023
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📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Counterfactual #Explanations #Evaluation #Challenges #survey
📘 journal: ACM Computing Surveys (I.F=16.6)
🗓Publish year: 2023
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📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Counterfactual #Explanations #Evaluation #Challenges #survey
👍3
📄Machine Learning for Anomaly Detection: A Systematic Review
📘 journal: IEEE Acess (I.F=3.476)
🗓Publish year: 2021
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📲Channel: @ComplexNetworkAnalysis
#paper #graph #Anomaly_detection #review
📘 journal: IEEE Acess (I.F=3.476)
🗓Publish year: 2021
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📲Channel: @ComplexNetworkAnalysis
#paper #graph #Anomaly_detection #review
🔥3
📄 Survey of Deep Graph Clustering: Taxonomy,Challenge, Application, and Open Resource
🗓Publish year: 2023
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📱Channel: @ComplexNetworkAnalysis
#paper #Deep #Graph #Clustering #Taxonomy #Challenge #Application #Open_Resource #survey
🗓Publish year: 2023
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#paper #Deep #Graph #Clustering #Taxonomy #Challenge #Application #Open_Resource #survey
❤4👍1
📄Information cascades in complex networks
📘 journal: Journal of Complex Networks (I.F=1.492)
🗓Publish year: 2017
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📲Channel: @ComplexNetworkAnalysis
#paper #graph #cascades #review
📘 journal: Journal of Complex Networks (I.F=1.492)
🗓Publish year: 2017
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#paper #graph #cascades #review
👍5👏1
📄Spatial social network research: a bibliometric analysis
📘 journal: Computational Urban Science
🗓Publish year: 2022
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📱Channel: @ComplexNetworkAnalysis
#paper #Spatial #research #bibliometric
📘 journal: Computational Urban Science
🗓Publish year: 2022
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#paper #Spatial #research #bibliometric
❤2
📚Graph Theory Notes
🧑💼 author: Vadim Lozin in Institute of Mathematics University of Warwick
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#Booklet #graph
🧑💼 author: Vadim Lozin in Institute of Mathematics University of Warwick
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#Booklet #graph
👍3
📄Complex systems and network science: a survey
📘 journal: Journal of Systems Engineering and Electronics (I.F=2.1)
🗓Publish year: 2023
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📱Channel: @ComplexNetworkAnalysis
#paper #Complex_systems #network_science #survey
📘 journal: Journal of Systems Engineering and Electronics (I.F=2.1)
🗓Publish year: 2023
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#paper #Complex_systems #network_science #survey
👍2❤1👏1
📚 Knowledge Graphs
✨This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale.
🧑💼 authors: Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, Jose Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M Rashid, Anisa Rula, Juan Sequeda, Lukas Schmelzeisen, Steffen Staab, Antoine Zimmerman
🗓Publish year: 2021
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📲Channel: @ComplexNetworkAnalysis
#Book #graph #Data_Graphs #Graph_Algorithms #Graph_Analytics #Graph_Neural_Networks #Knowledge_Graphs #Social_Networks
✨This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale.
🧑💼 authors: Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, Jose Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M Rashid, Anisa Rula, Juan Sequeda, Lukas Schmelzeisen, Steffen Staab, Antoine Zimmerman
🗓Publish year: 2021
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📲Channel: @ComplexNetworkAnalysis
#Book #graph #Data_Graphs #Graph_Algorithms #Graph_Analytics #Graph_Neural_Networks #Knowledge_Graphs #Social_Networks
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