Network Analysis Resources & Updates
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🎞 Graph Theory Algorithms
💥A complete overview of graph theory algorithms in computer science and mathematics.
📽Watch
📲Channel: @ComplexNetworkAnalysis
#video #Graph #course
📄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 #establishment #export #trade #petrochemical
📄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
📄Graph-based Time-Series Anomaly Detection: A Survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Time_Series #Anomaly #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
📄A Network Science perspective of Graph Convolutional Networks: A survey
📘Journal: FUTURE INTERNET
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #perspective #Convolutional #survey
🎞 Knowledge Graph Seminar Session 1 (Spring 2020)
💥Free recorded tutorial on Knowledge Graph.
📽Watch
📱Channel: @ComplexNetworkAnalysis
#video #Knowledge_Graph #seminar
📄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
📄A Mini review of Node Centrality Metrics in Biological Networks
🔸Abstract: The diversity of nodes in a complex network causes each node to have varying significance, and the important nodes often have a significant impact on the structure and function of the network. Although the interpretation of the results of biological networks must always depend on the topological study of nodes, there is presently no consensus on how to use these metrics, and most network analyses always result in a basic interpretation of a limited number of metrics. To thoroughly comprehend biologi cal networks, it is necessary to consistently understand the notion of node centrality. Therefore, for 10 typical nodal metrics in biological networks, the study first assesses their current applications, advan tages, disadvantages as well as potential applications. Then, a review of previous studies is provided, and suggestions are made correspondingly for the purpose of improving biological topology algorithms. Finally, the following recommendations are made in this study: (1) a comprehensive and accurate assess ment of node centrality necessitates the use of multiple metrics, including both the target node and its surroundings, and density of maximum neighbourhood component (DMNC) can be used as a comple ment to other node centrality metrics; (2) different centrality metrics can be applied to identify nodes with different functions, which in this study are mapped as modular surroundings, bridging roles, and susceptibility; and (3) the following groups of node centrality can often be verified against each other, including degree and maximum neighbourhood component (MNC), eccentricity, closeness and radiality; stress and betweenness.
📘Journal: International Journal of Network Dynamics and Intelligence
🗓Publish year: 2022
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
👨💻 MSc position at SBNA (Social & Biological Network Analysis) Lab
🇮🇷 Language: IR
🌐 Details
📲Channel: @ComplexNetworkAnalysis
📄Complex Network Analysis of China National Standards for New Energy Vehicles
📘Journal: Sustainability(I.F=889)
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper
📄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
📄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
📄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
📄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
📄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
📄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
📄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
🎞 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
🎞 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
