📄Privacy issues in social networks and analysis: a comprehensive survey
📘Journal: IET NETWORKS
🗓Publish year: 2018
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Privacy #survey
📘Journal: IET NETWORKS
🗓Publish year: 2018
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Privacy #survey
📄A Survey Of Link Prediction In Social Network Using Deep Learning Approach
📘Journal: International Journal of Scientific & Technology Research
🗓Publish year: 2020
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Deep_Learning
📘Journal: International Journal of Scientific & Technology Research
🗓Publish year: 2020
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #Deep_Learning
📄Survey of Graph Neural Networks and Applications
📘Journal: Wireless Communications and Mobile Computing (I.F=2.146)
🗓Publish year: 2022
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Survey #Applications
📘Journal: Wireless Communications and Mobile Computing (I.F=2.146)
🗓Publish year: 2022
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Survey #Applications
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📄Social network analysis in Telecom data
📘Journal: Big Data (I.F=10.835)
🗓Publish year: 2019
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Telecom
📘Journal: Big Data (I.F=10.835)
🗓Publish year: 2019
📎 Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Telecom
🎓Social network analysis approaches to study crime
📘Doctoral thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy in Mathematics and Computational Sciences
🗓Publish year: 2022
📎 Study
📱Channel: @ComplexNetworkAnalysis
#thesis #crime
📘Doctoral thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy in Mathematics and Computational Sciences
🗓Publish year: 2022
📎 Study
📱Channel: @ComplexNetworkAnalysis
#thesis #crime
📄Covert Network Construction, Disruption, and Resilience: A Survey
📘Journal: MATHEMATICS (I.F= 2.592)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Covert_Network #Resilience #Survey
📘Journal: MATHEMATICS (I.F= 2.592)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Covert_Network #Resilience #Survey
📄Social network analysis for social neuroscientists
📘Journal: SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE
(I.F= 4.235)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #neuroscientists
📘Journal: SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE
(I.F= 4.235)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #neuroscientists
📄Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications
🗓Publish year: 2020
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Signal_Processing #Machine_Learning
🗓Publish year: 2020
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Signal_Processing #Machine_Learning
📄How to get started with Graph Machine Learning
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
Medium
How to get started with Graph Machine Learning
Deep learning update: What have I learned about Graph ML in 2 months?
2020_Linking_Network_Characteristics_of_Online_Social_Networks_to.pdf
613.7 KB
📄Linking Network Characteristics of Online Social Networks to Individual Health: A Systematic Review of Literature
📘Journal: Health Communication (I.F= 3.501)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Health #review
📘Journal: Health Communication (I.F= 3.501)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Health #review
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🎞 Graph-Powered Machine Learning
💥Free recorded Lecture
💥Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.
Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Lecture #Machine_Learning
💥Free recorded Lecture
💥Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.
Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #Lecture #Machine_Learning
YouTube
Graph-Powered Machine Learning
Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds…
📄Applications of Graph Neural Networks
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Neural_Networks #GNN
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Neural_Networks #GNN
Medium
Applications of Graph Neural Networks
Exploring the forays of GNN based techniques into diverse domains
📄Survey on graph embeddings and their applications to machine learning problems on graphs
📘Journal: PeerJ Computer Science (I.F= 2.41)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #graph_embedding #Machine_Learning
📘Journal: PeerJ Computer Science (I.F= 2.41)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #graph_embedding #Machine_Learning
2018_Opinion leader detection A methodological review.pdf
7.7 MB
📄Opinion leader detection: A methodological review
📘Journal: EXPERT SYSTEMS WITH APPLICATIONS (I.F=8.665)
🗓Publish year: 2018
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #leader #review
📘Journal: EXPERT SYSTEMS WITH APPLICATIONS (I.F=8.665)
🗓Publish year: 2018
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #leader #review
📄Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Acceleration #Survey
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Acceleration #Survey
📄Representation Learning on Graphs: Methods and Applications
📘Journal: IEEE Data Engineering Bulletin
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Representation_Learning
📘Journal: IEEE Data Engineering Bulletin
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Representation_Learning
🎞 Graph Search, Shortest Paths, and Data Structures
💥Free recorded course by Tim Roughgarden
💥The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph
💥Free recorded course by Tim Roughgarden
💥The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph
Coursera
Graph Search, Shortest Paths, and Data Structures
Offered by Stanford University. The primary topics in ... Enroll for free.
📘 Graph Representation Learning
💥Free online book by William L. Hamilton
🗓Publish year: 2020
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#book #Graph
💥Free online book by William L. Hamilton
🗓Publish year: 2020
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#book #Graph
📄Machine learning on Graphs course: Pre-requisites
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Graph #TensorFlow
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Graph #TensorFlow
Medium
Machine learning on Graphs course: Pre-requisites
(This is part of a four part course hosted by Octavian.ai this summer)
📄Deep Graph Learning: Foundations, Advances and Applications
📘Conference: 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
📘Conference: 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph
📘 Network Science
💥Free online book by Albert-László Barabási
💥The book is the result of a collaboration between a number of individuals, shaping everything, from content (Albert-László Barabási), to visualizations and interactive tools (Gabriele Musella, Mauro Martino, Nicole Samay, Kim Albrecht), simulations and data analysis (Márton Pósfai). The printed version of the book will be published by Cambridge University Press in 2015. In the coming months the website will be expanded with an interactive version of the text, datasets, and slides to teach the material.
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#online_book
💥Free online book by Albert-László Barabási
💥The book is the result of a collaboration between a number of individuals, shaping everything, from content (Albert-László Barabási), to visualizations and interactive tools (Gabriele Musella, Mauro Martino, Nicole Samay, Kim Albrecht), simulations and data analysis (Márton Pósfai). The printed version of the book will be published by Cambridge University Press in 2015. In the coming months the website will be expanded with an interactive version of the text, datasets, and slides to teach the material.
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#online_book
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