2022_Knowledge_Graphs_A_Practical_Review_of_the_Research_Landscape.pdf
510 KB
📄Knowledge Graphs: A Practical Review of the Research Landscape
📘Journal: INFORMATION
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Research #Landscape #review
📘Journal: INFORMATION
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Research #Landscape #review
📄Knowledge Graph Completion: A Bird’s Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Embeddings #Software #Applications #Challenges
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Embeddings #Software #Applications #Challenges
🎞 Machine Learning with Graphs: PageRank Random Walks and embedding
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, -we will talk about an alternative approach, message passing. We will introduce the semi-supervised learning on predicting node labels by leveraging correlations that exist in the network. One key concept is the collective classification, which involves three steps including the local classifier that assigns initial labels, the relational classifier that captures correlations, and the collective inference that propagates correlations.
-we introduce belief propagation, which is a dynamic programming approach to answering probability queries in a graph. By iteratively passing messages to neighbors, the final belief is calculated if a consensus is reached. We then show the message passing with examples and generalization to tree structure. At last, we talk about the loopy belief propagation algorithm, and its pros and cons.
-we introduce the relational classifier and iterative classification for node classification. Starting from the relational classifier, we show how to iteratively update probabilities of node labels based on the labels of neighbors. We then talk about the iterative classification that improves the collective classification by predicting node label based on labels of neighbors as well as its features
📽 Watch: part1 part2 part3
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, -we will talk about an alternative approach, message passing. We will introduce the semi-supervised learning on predicting node labels by leveraging correlations that exist in the network. One key concept is the collective classification, which involves three steps including the local classifier that assigns initial labels, the relational classifier that captures correlations, and the collective inference that propagates correlations.
-we introduce belief propagation, which is a dynamic programming approach to answering probability queries in a graph. By iteratively passing messages to neighbors, the final belief is calculated if a consensus is reached. We then show the message passing with examples and generalization to tree structure. At last, we talk about the loopy belief propagation algorithm, and its pros and cons.
-we introduce the relational classifier and iterative classification for node classification. Starting from the relational classifier, we show how to iteratively update probabilities of node labels based on the labels of neighbors. We then talk about the iterative classification that improves the collective classification by predicting node label based on labels of neighbors as well as its features
📽 Watch: part1 part2 part3
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 5.1 - Message passing and Node Classification
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jHRiGj
Jure Leskovec
Computer Science, PhD
From previous lectures, we learn the use of graph representation learning for node classification.…
Jure Leskovec
Computer Science, PhD
From previous lectures, we learn the use of graph representation learning for node classification.…
📄Taxonomy of Link Prediction for Social Network Analysis: A Review
📘Journal: IEEE Access (I.F=3.476)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Taxonomy #Link_Prediction #review
📘Journal: IEEE Access (I.F=3.476)
🗓Publish year: 2020
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Taxonomy #Link_Prediction #review
📄Knowledge graph and knowledge reasoning: A systematic review
📘Journal: Journal of Electronic Science and Technology
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_graph #review
📘Journal: Journal of Electronic Science and Technology
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_graph #review
Knowledge_Graph_Embedding_A_Survey_of_Approaches_and_Applications.pdf
970.4 KB
📄Knowledge Graph Embedding: A Survey of Approaches and Applications
📘Journal: IEEE Transactions on Knowledge and Data Engineering(I.F=6.997)
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📘Journal: IEEE Transactions on Knowledge and Data Engineering(I.F=6.997)
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📄Gamification in education: A citation network analysis using
CitNetExplorer
📘Journal: Contemporary Educational Technology(I.F=3.68)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #CitNetExplorer
CitNetExplorer
📘Journal: Contemporary Educational Technology(I.F=3.68)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #CitNetExplorer
📄Complex Network Analysis of China National Standards for New Energy Vehicles
📘Journal: Sustainability(I.F=3.889)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📘Journal: Sustainability(I.F=3.889)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
👨💻 MSc position at SBNA (Social & Biological Network Analysis) Lab
🇮🇷 Language: IR
🌐 Details
📲Channel: @ComplexNetworkAnalysis
🇮🇷 Language: IR
🌐 Details
📲Channel: @ComplexNetworkAnalysis
📄A Mini review of Node Centrality Metrics in Biological Networks
📘Journal: International Journal of Network Dynamics and Intelligence
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #centrality #biological
📘Journal: International Journal of Network Dynamics and Intelligence
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #centrality #biological
👍3
🎞 Knowledge Graph Seminar Session 1 (Spring 2020)
💥Free recorded tutorial on Knowledge Graph.
📽Watch
📱Channel: @ComplexNetworkAnalysis
#video #Knowledge_Graph #seminar
💥Free recorded tutorial on Knowledge Graph.
📽Watch
📱Channel: @ComplexNetworkAnalysis
#video #Knowledge_Graph #seminar
YouTube
CS520: Knowledge Graph Seminar Session 1 (Spring 2020)
What is a Knowledge Graph?
📄A Network Science perspective of Graph Convolutional Networks: A survey
📘Journal: FUTURE INTERNET
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #perspective #Convolutional #survey
📘Journal: FUTURE INTERNET
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #perspective #Convolutional #survey
📄Network Analysis of Road Traffic Crash and Rescue Operations in Federal Capital City
📘Journal: International Journal of Geosciences (I.F=1.525)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Traffic
📘Journal: International Journal of Geosciences (I.F=1.525)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Traffic
📄Graph-based Time-Series Anomaly Detection: A Survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Time_Series #Anomaly #survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Time_Series #Anomaly #survey
📄Women financial inclusion research: a bibliometric and network analysis
📘Journal: INTERNATIONAL JOURNAL OF SOCIAL ECONOMICS
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Women #financial #inclusion #bibliometric
📘Journal: INTERNATIONAL JOURNAL OF SOCIAL ECONOMICS
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Women #financial #inclusion #bibliometric
📄Predicting the establishment and removal of global trade relations for import and export of petrochemical products
📘Journal: Energy (I.F=8.857)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #prediction #trade #petrochemical
📘Journal: Energy (I.F=8.857)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #prediction #trade #petrochemical
🎞 Graph Theory Algorithms
💥A complete overview of graph theory algorithms in computer science and mathematics.
📽Watch
📲Channel: @ComplexNetworkAnalysis
#video #Graph #course
💥A complete overview of graph theory algorithms in computer science and mathematics.
📽Watch
📲Channel: @ComplexNetworkAnalysis
#video #Graph #course
Udemy
Graph Theory Algorithms
A complete overview of graph theory algorithms in computer science and mathematics.
📄Graph Clustering with Graph Neural Networks
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Clustering #GNN
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #Clustering #GNN
👍4
🎞📙Network Analysis Made Simple
💥Network Analysis Made Simple is a collection of Jupyter notebooks designed to help you get up and running with the NetworkX package in the Python programming langauge. It's written by programmers for programmers, and will give you a basic introduction to graph theory, applied network science, and advanced topics to help kickstart your learning journey. There's even case studies to help those of you for whom example narratives help a ton!
📽Watch & study
📲Channel: @ComplexNetworkAnalysis
#video #Graph #course #python #code #ebook
💥Network Analysis Made Simple is a collection of Jupyter notebooks designed to help you get up and running with the NetworkX package in the Python programming langauge. It's written by programmers for programmers, and will give you a basic introduction to graph theory, applied network science, and advanced topics to help kickstart your learning journey. There's even case studies to help those of you for whom example narratives help a ton!
📽Watch & study
📲Channel: @ComplexNetworkAnalysis
#video #Graph #course #python #code #ebook
👍4
📄Curriculum Graph Machine Learning: A Survey
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #Machine_Learning #Graph
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #Machine_Learning #Graph
👍2
📄Relative, local and global dimension in complex networks
📘Journal: NATURE COMMUNICATIONS (I.F=17.694)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Relative #local #global #dimension
📘Journal: NATURE COMMUNICATIONS (I.F=17.694)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Relative #local #global #dimension