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📘 Deep Learning on Graphs
💥Free online book by Yao Ma and Jiliang Tang
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#book #Graph #Deep_Learning
+1📄Utilizing graph machine learning within drug discovery and development
📘Journal: Briefings in Bioinformatics(I.F=11.622
)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #machine_learning
🎞 Machine learning on graphs
💥Free recorded course by Alexander S. Kulikov
💥The course has a couple of components:
▪️Projects - Google Colab documents that guide you through writing python and TensorFlow code to solve problems.
▪️Project solutions - A week after a project is published, the solution will be published. It'll be linked to from the original project so as not to spoil the project for new visitors.
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_learning #code #python
📄Survey on Opinion Leader in Social Network using Data Mining
📘Conference: 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)
🗓Publish year: 2019
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Data_Mining
📘 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
📄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
📄Machine learning on Graphs course: Pre-requisites
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Graph #TensorFlow
📘 Graph Representation Learning
💥Free online book by William L. Hamilton
🗓Publish year: 2020
📎 Study the book
📲Channel: @ComplexNetworkAnalysis
#book #Graph
🎞 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
📄Representation Learning on Graphs: Methods and Applications
📘Journal: IEEE Data Engineering Bulletin
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Representation_Learning
📄Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
📘Journal: Computer Science
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Acceleration #Survey
📄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
📄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
📄Applications of Graph Neural Networks
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Neural_Networks #GNN
🎞 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
📄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
📄How to get started with Graph Machine Learning
💥Technical paper
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning
📄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
📄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
📄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
