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723 - Telegram Web
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๐Ÿ“„Knowledge Graph Completion: A Birdโ€™s Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges

๐Ÿ—“Publish year: 2022

๐Ÿ“ŽStudy paper

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis
#paper #Knowledge_Graphs #Embeddings #Software #Applications #Challenges
๐ŸŽž Machine Learning with Graphs: PageRank Random Walks and embedding

๐Ÿ’ฅFree recorded course by Jure Leskovec, Computer Science, PhD

๐Ÿ’ฅIn 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

๐Ÿ“ฝ Watch: part1 part2 part3

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning
๐Ÿ“„Taxonomy of Link Prediction for Social Network Analysis: A Review

๐Ÿ“˜
Journal: IEEE Access (I.F=3.476)
๐Ÿ—“
Publish year: 2020

๐Ÿ“ŽStudy paper

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis
#paper #Taxonomy #Link_Prediction #review
๐Ÿ“„Knowledge graph and knowledge reasoning: A systematic review

๐Ÿ“˜
Journal: Journal of Electronic Science and Technology
๐Ÿ—“
Publish year: 2022

๐Ÿ“ŽStudy paper

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis
#paper #Knowledge_graph #review
Knowledge_Graph_Embedding_A_Survey_of_Approaches_and_Applications.pdf
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๐Ÿ“„Knowledge Graph Embedding: A Survey of Approaches and Applications

๐Ÿ“˜Journal: IEEE Transactions on Knowledge and Data Engineering(I.F=6.997)

๐Ÿ—“Publish year: 2017

๐Ÿ“ŽStudy paper

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
#paper
๐Ÿ“„Gamification in education: A citation network analysis using
CitNetExplorer

๐Ÿ“˜Journal: Contemporary Educational Technology(I.F=3.68)

๐Ÿ—“Publish year: 2023

๐Ÿ“ŽStudy paper

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
#paper #CitNetExplorer
๐Ÿ“„Complex Network Analysis of China National Standards for New Energy Vehicles

๐Ÿ“˜Journal: Sustainability(I.F=3.889)

๐Ÿ—“Publish year: 2023

๐Ÿ“ŽStudy paper

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
#paper
๐Ÿ‘จโ€๐Ÿ’ป MSc position at SBNA (Social & Biological Network Analysis) Lab

๐Ÿ‡ฎ๐Ÿ‡ท Language: IR

๐ŸŒ Details

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
๐Ÿ“„A Mini review of Node Centrality Metrics in Biological Networks

๐Ÿ“˜Journal: International Journal of Network Dynamics and Intelligence
๐Ÿ—“Publish year: 2022

๐Ÿ“ŽStudy paper

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
#paper #centrality #biological
๐ŸŽž Knowledge Graph Seminar Session 1 (Spring 2020)

๐Ÿ’ฅFree recorded tutorial on Knowledge Graph.

๐Ÿ“ฝWatch

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis

#video #Knowledge_Graph #seminar
๐Ÿ“„A Network Science perspective of Graph Convolutional Networks: A survey

๐Ÿ“˜
Journal: FUTURE INTERNET
๐Ÿ—“
Publish year: 2022

๐Ÿ“ŽStudy paper

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis
#paper #perspective #Convolutional #survey
๐Ÿ“„Network Analysis of Road Traffic Crash and Rescue Operations in Federal Capital City

๐Ÿ“˜Journal: International Journal of Geosciences (I.F=1.525)
๐Ÿ—“Publish year: 2023

๐Ÿ“ŽStudy paper

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
#paper #Traffic
๐Ÿ“„Graph-based Time-Series Anomaly Detection: A Survey

๐Ÿ—“Publish year: 2023

๐Ÿ“ŽStudy paper

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis
#paper #Time_Series #Anomaly #survey
๐Ÿ“„Women financial inclusion research: a bibliometric and network analysis

๐Ÿ“˜
Journal: INTERNATIONAL JOURNAL OF SOCIAL ECONOMICS
๐Ÿ—“
Publish year: 2023

๐Ÿ“ŽStudy paper

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis
#paper #Women #financial #inclusion #bibliometric
๐Ÿ“„Predicting the establishment and removal of global trade relations for import and export of petrochemical products

๐Ÿ“˜
Journal: Energy (I.F=8.857)
๐Ÿ—“
Publish year: 2023

๐Ÿ“ŽStudy paper

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis
#paper #prediction #trade #petrochemical
๐ŸŽž Graph Theory Algorithms

๐Ÿ’ฅA complete overview of graph theory algorithms in computer science and mathematics.

๐Ÿ“ฝWatch

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis

#video #Graph #course
๐Ÿ“„Graph Clustering with Graph Neural Networks

๐Ÿ—“Publish year: 2020

๐Ÿ“ŽStudy paper

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
#paper #Graph #Clustering #GNN
๐ŸŽž๐Ÿ“™Network Analysis Made Simple

๐Ÿ’ฅNetwork 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!

๐Ÿ“ฝWatch & study

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis

#video #Graph #course #python #code #ebook
๐Ÿ“„Curriculum Graph Machine Learning: A Survey

๐Ÿ—“Publish year: 2023

๐Ÿ“ŽStudy paper

๐Ÿ“ฒChannel: @ComplexNetworkAnalysis
#paper #Survey #Machine_Learning #Graph
๐Ÿ“„Relative, local and global dimension in complex networks

๐Ÿ“˜
Journal: NATURE COMMUNICATIONS (I.F=17.694)
๐Ÿ—“
Publish year: 2022

๐Ÿ“ŽStudy paper

๐Ÿ“ฑChannel: @ComplexNetworkAnalysis
#paper #Relative #local #global #dimension
2025/07/07 23:03:54
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