📄Identifying spreading influence nodes for social networks
📘journal: Frontiers of Engineering Management (FEM)(I.F=7.4)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Identifying #spreading #influence_nodes
📘journal: Frontiers of Engineering Management (FEM)(I.F=7.4)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Identifying #spreading #influence_nodes
📄A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
🗓Publish year: 2021
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #Survey #Neural_Network #Forecasting #Anomaly_Detection
🗓Publish year: 2021
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN #Survey #Neural_Network #Forecasting #Anomaly_Detection
👏2👍1
🎞 Social Network Analysis - network structure
💥Free recorded lecture from UCCSS (University of California Computational Social Sciences)
🔹This lecture is part of the University of California wide online course on Computational Social Science (UCCSS), produced with input from Professors from all 10 UC campuses and offered to UC students for credit since 2018. For more on this topic, see the open Online Specialization link.
📽 Watch
💻Open Online Specialization
📱Channel: @ComplexNetworkAnalysis
#video #network_structure
💥Free recorded lecture from UCCSS (University of California Computational Social Sciences)
🔹This lecture is part of the University of California wide online course on Computational Social Science (UCCSS), produced with input from Professors from all 10 UC campuses and offered to UC students for credit since 2018. For more on this topic, see the open Online Specialization link.
📽 Watch
💻Open Online Specialization
📱Channel: @ComplexNetworkAnalysis
#video #network_structure
YouTube
UCCSS Hilbert SNA1: Social Network Analysis - network structure
This lecture is part of the University of California wide online course on Computational Social Science (UCCSS), produced with input from Professors from all 10 UC campuses and offered to UC students for credit since 2018. For more on this topic, see the…
👍4
📄Construction of Knowledge Graphs: Current State and Challenges
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph
👍2
📄Fairness-Aware Graph Neural Networks: A Survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Fairness #Graph_Neural_Networks #survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Fairness #Graph_Neural_Networks #survey
🎞 Machine Learning with Graphs: Reasoning in Knowledge Graphs, Answering Predictive Queries, Query2box: Reasoning over KGs
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 IIn this lecture, we introduce how to perform reasoning over knowledge graphs and provide answers to complex queries. We talk about different possible queries that one can get over a knowledge graph, and how to answer them by traversing over the graph. We also show how incompleteness of knowledge graphs can limit our ability to provide complete answers. We finally talk about how we can solve this problem by generalizing the link prediction task.
📽 Watch: part1 part2 part3
📝 slide
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥 IIn this lecture, we introduce how to perform reasoning over knowledge graphs and provide answers to complex queries. We talk about different possible queries that one can get over a knowledge graph, and how to answer them by traversing over the graph. We also show how incompleteness of knowledge graphs can limit our ability to provide complete answers. We finally talk about how we can solve this problem by generalizing the link prediction task.
📽 Watch: part1 part2 part3
📝 slide
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Knowledge_Graph
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 11.1 - Reasoning in Knowledge Graphs
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3BweHQZ
Lecture 11.1 - Reasoning in Knowledge Graphs using Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture, we introduce…
Lecture 11.1 - Reasoning in Knowledge Graphs using Embeddings
Jure Leskovec
Computer Science, PhD
In this lecture, we introduce…
👍1
📄Construction of Knowledge Graphs: Current State and Challenges
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Current_State
#Challenges
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Current_State
#Challenges
❤2👍2
🎓Graph entropy and related topics
📘Phd’s Dissertation, at the University of Twente.
🗓Publish year: 2023
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Graph #Network_Comparison
📘Phd’s Dissertation, at the University of Twente.
🗓Publish year: 2023
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Graph #Network_Comparison
👍6
📄Everything is Connected: Graph Neural Networks
📘Journal: Current opinion in structural biology (l.F=7.876)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN
📘Journal: Current opinion in structural biology (l.F=7.876)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #GNN
👍1
📄Network Analysis of Time Series: Novel Approaches to Network Neuroscience
📘journal :Frontiers in Neuroscience (I.F= 4.3)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Time_Series #Neuroscience
📘journal :Frontiers in Neuroscience (I.F= 4.3)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Time_Series #Neuroscience
📄A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
📘journal: ACM Transactions on Recommender Systems (l.F=4.657)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN #Recommender_Systems
📘journal: ACM Transactions on Recommender Systems (l.F=4.657)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Survey #GNN #Recommender_Systems
📄A Comprehensive Survey on Graph Neural Networks
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Neural_Networks #survey
🗓Publish year: 2019
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Neural_Networks #survey
📄Summary of Static Graph Embedding Algorithms
📘Conference: 2023 4th International Conference on Computer Vision, Image and Deep Learning (CVIDL)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #Summary
📘Conference: 2023 4th International Conference on Computer Vision, Image and Deep Learning (CVIDL)
🗓Publish year: 2023
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #Summary
📄Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends
📘journal: HEALTHCARE-BASEL (I.F=2.8)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Disease #Prediction #Graph_Machine_Learning #Electronic #Health #Trends #Review
📘journal: HEALTHCARE-BASEL (I.F=2.8)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Disease #Prediction #Graph_Machine_Learning #Electronic #Health #Trends #Review
❤1
📄Automated Machine Learning on Graphs: A Survey
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Automated_Machine_Learning #Survey
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Automated_Machine_Learning #Survey
🎞 Machine Learning with Graphs: Neural Subgraph Matching & Counting, Neural Subgraph Matching, Finding Frequent Subgraphs
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, we will be talking about the problem on subgraph matching and counting. Subgraphs work as building blocks for larger networks, and have the power to characterize and discriminate networks. We first give an introduction on two types of subgraphs - node-induced subgraphs and edge-induced subgraphs. Then we give you an idea how to determine subgraph relation through the concept of graph isomorphism. Finally, we discuss why subgraphs are important, and how we can identify the most informative subgraphs with network significance profile.
📽 Watch: part1 part2 part3
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Subgraph
💥Free recorded course by Jure Leskovec, Computer Science, PhD
💥In this lecture, we will be talking about the problem on subgraph matching and counting. Subgraphs work as building blocks for larger networks, and have the power to characterize and discriminate networks. We first give an introduction on two types of subgraphs - node-induced subgraphs and edge-induced subgraphs. Then we give you an idea how to determine subgraph relation through the concept of graph isomorphism. Finally, we discuss why subgraphs are important, and how we can identify the most informative subgraphs with network significance profile.
📽 Watch: part1 part2 part3
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #Subgraph
YouTube
CS224W: Machine Learning with Graphs | 2021 | Lecture 12.1-Fast Neural Subgraph Matching & Counting
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jR7jK2
Jure Leskovec
Computer Science, PhD
In this lecture, we will be talking about the problem on subgraph matching and counting.…
Jure Leskovec
Computer Science, PhD
In this lecture, we will be talking about the problem on subgraph matching and counting.…
📄Recent Advances in Network-based Methods for
Disease Gene Prediction
📘journal: Briefings in bioinformatics (I.F= 9.5)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Advances #Network_based_Methods #Disease #Gene #Prediction
Disease Gene Prediction
📘journal: Briefings in bioinformatics (I.F= 9.5)
🗓Publish year: 2021
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Advances #Network_based_Methods #Disease #Gene #Prediction
Forwarded from Bioinformatics
🎓 Towards causality in gene regulatory network inference
📔PhD Thesis from Massachusetts Institute of Technology
🗓Publish year: 2023
📎 Study thesis
📲Channel: @Bioinformatics
#thesis #gene_regulatory
📔PhD Thesis from Massachusetts Institute of Technology
🗓Publish year: 2023
📎 Study thesis
📲Channel: @Bioinformatics
#thesis #gene_regulatory
📄A Survey on Graph Classification and Link Prediction based on GNN
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Classification #Link_Prediction #GNN #Survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Classification #Link_Prediction #GNN #Survey
👍1
🎓Embedding of Dynamical Networks
📘Phd’s Dissertation, at the Engineering and Maths RMIT University
🗓Publish year: 2022
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Graph #Embedding
📘Phd’s Dissertation, at the Engineering and Maths RMIT University
🗓Publish year: 2022
📎Study Dissertation
📲Channel: @ComplexNetworkAnalysis
#Dissertation #Graph #Embedding
📄Graphs in computer graphics
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graphs #computer_graphics
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graphs #computer_graphics