Network Analysis Resources & Updates - Telegram 频道 @complexnetworkanalysis 的统计与分析

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📄Complex Network Analysis of China National Standards for New Energy Vehicles 📘Journal: Sustainability(I.F=889) 🗓Publish year: 2023 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper

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📄Complex Network Analysis of China National Standards for New Energy Vehicles 📘Journal: Sustainability(I.F=889) 🗓Publish year: 2023 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #Energy_Vehicles

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📄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

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📄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

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📄Knowledge Graph Embedding: A Survey of Approaches and Applications 📘Journal: IEEE Transactions on Knowledge and Data Engineering(I.F=) 🗓Publish year: 2017 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper

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📄Knowledge Graph Embedding: A Survey of Approaches and Applications 📘Journal: IEEE Transactions on Knowledge and Data Engineering(I.F=) 🗓Publish year: 2017 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper

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📄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

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📄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

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🎞 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

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🎞 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

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📄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

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📄Knowledge Graphs: A Practical Review of the Research Landscape 📘Journal: INFORMATION 🗓Publish year: 2022 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Knowledge_Graphs #Research #Landscape #review

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📄Network analysis on political election; populist vs social emergent behaviour 🗓Publish year: 2023 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper

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📄Network Analysis for the Digital Humanities: Principles, Problems, Extensions 📘Journal: ISIS 🗓Publish year: 2019 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Digital #Humanities #Principles #Problems #Extensions

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📄A Note on Graph-Based Nearest Neighbor Search 🗓Publish year: 2020 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #Graph

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📄An Introduction to Graph Theory 💥Technical paper 🌐 Study 📲Channel: @ComplexNetworkAnalysis #paper #Graph

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🎞 Graph Theory: Nearest Neighbor Algorithm (NNA) 💥Free recorded tutorial 🔹This tutorial is about Nearest neighbour algorithm, Travelling salesman problem, Heuristic, Hamiltonian path 📽 Watch 📲Channel: @ComplexNetworkAnalysis #video #Graph

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📄Graph Learning Approaches to Recommender Systems: A Review 📘Journal: Information Retrieval 🗓Publish year: 2020 📎Study paper 📱Channel: @ComplexNetworkAnalysis #paper #Recommender_Systems #Graph #review

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📄On Anomaly Detection in Graphs as Node Classification 📘Conference: Big Data Management and Analysis for Cyber Physical Systems 🗓Publish year: 2022 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #graph

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📄Social Network Theory in Construction Industry: A Scientometric Review 📘Conference: Recent Trends in Civil Engineering 🗓Publish year: 2022 📎Study paper 📲Channel: @ComplexNetworkAnalysis #paper #Review #Social_Network