📄Influence maximization in social networks: a survey of behaviour-aware methods
📘 journal: Social Network Analysis and Mining (SNAM) (I.F=2.8)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Influence #maximization #behaviour_aware #survey
📘 journal: Social Network Analysis and Mining (SNAM) (I.F=2.8)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Influence #maximization #behaviour_aware #survey
📄Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Privacy #Preserving #Graph_Machine_Learning #Computation #survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Privacy #Preserving #Graph_Machine_Learning #Computation #survey
📄 A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #survey #GNN #anomaly_detection #time_series
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #survey #GNN #anomaly_detection #time_series
📄A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges
📘 journal: ACM Computing Surveys (I.F=16.6)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Counterfactual #Explanations #Evaluation #Challenges #survey
📘 journal: ACM Computing Surveys (I.F=16.6)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph #Counterfactual #Explanations #Evaluation #Challenges #survey
📄Machine Learning for Anomaly Detection: A Systematic Review
📘 journal: IEEE Acess (I.F=3.476)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #Anomaly_detection #review
📘 journal: IEEE Acess (I.F=3.476)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #Anomaly_detection #review
📄 Survey of Deep Graph Clustering: Taxonomy,Challenge, Application, and Open Resource
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Deep #Graph #Clustering #Taxonomy #Challenge #Application #Open_Resource #survey
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Deep #Graph #Clustering #Taxonomy #Challenge #Application #Open_Resource #survey
📄Information cascades in complex networks
📘 journal: Journal of Complex Networks (I.F=1.492)
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #cascades #review
📘 journal: Journal of Complex Networks (I.F=1.492)
🗓Publish year: 2017
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #cascades #review
📄Spatial social network research: a bibliometric analysis
📘 journal: Computational Urban Science
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Spatial #research #bibliometric
📘 journal: Computational Urban Science
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Spatial #research #bibliometric
📚Graph Theory Notes
🧑💼 author: Vadim Lozin in Institute of Mathematics University of Warwick
📎Study
📲Channel: @ComplexNetworkAnalysis
#Booklet #graph
🧑💼 author: Vadim Lozin in Institute of Mathematics University of Warwick
📎Study
📲Channel: @ComplexNetworkAnalysis
#Booklet #graph
📄Complex systems and network science: a survey
📘 journal: Journal of Systems Engineering and Electronics (I.F=2.1)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Complex_systems #network_science #survey
📘 journal: Journal of Systems Engineering and Electronics (I.F=2.1)
🗓Publish year: 2023
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Complex_systems #network_science #survey
📚 Knowledge Graphs
✨This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale.
🧑💼 authors: Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, Jose Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M Rashid, Anisa Rula, Juan Sequeda, Lukas Schmelzeisen, Steffen Staab, Antoine Zimmerman
🗓Publish year: 2021
📎Study
📲Channel: @ComplexNetworkAnalysis
#Book #graph #Data_Graphs #Graph_Algorithms #Graph_Analytics #Graph_Neural_Networks #Knowledge_Graphs #Social_Networks
✨This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale.
🧑💼 authors: Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia D'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, Jose Emilio Labra Gayo, Roberto Navigli, Sebastian Neumaier, Axel-Cyrille Ngonga Ngomo, Axel Polleres, Sabbir M Rashid, Anisa Rula, Juan Sequeda, Lukas Schmelzeisen, Steffen Staab, Antoine Zimmerman
🗓Publish year: 2021
📎Study
📲Channel: @ComplexNetworkAnalysis
#Book #graph #Data_Graphs #Graph_Algorithms #Graph_Analytics #Graph_Neural_Networks #Knowledge_Graphs #Social_Networks
📄Wolfram MathWorld
💥Technical online booklet and workspace
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#online_book #Graph #Graph_Theory
💥Technical online booklet and workspace
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#online_book #Graph #Graph_Theory
📄New Developments in Social Network Analysis
📘 journal: Annual Review of Organizational Psychology and Organizational Behavior (I.F=13.7)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Developments
📘 journal: Annual Review of Organizational Psychology and Organizational Behavior (I.F=13.7)
🗓Publish year: 2022
📎Study paper
📱Channel: @ComplexNetworkAnalysis
#paper #Developments
🎞 Community Detection in R in 2021 and Beyond, Part 1
💥2021 Social Networks Workshop
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Community_Detection #R
💥2021 Social Networks Workshop
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Community_Detection #R
YouTube
Community Detection in R in 2021 and Beyond, Part 1
2020_Graph_weeds_net_A_graph_based_deep_learning_method_for_weed.pdf
2.7 MB
📄Graph weeds net: A graph-based deep learning method for weed recognition
📘 journal: Computers and Electronics in Agriculture (I.F=6.757)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #deep_learnin #weed_recognition
📘 journal: Computers and Electronics in Agriculture (I.F=6.757)
🗓Publish year: 2020
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #deep_learnin #weed_recognition
2021-Graphnet Graph Clustering with Deep Neural Networks.pdf
2.3 MB
📄Graphnet: Graph Clustering with Deep Neural Networks
📘 Conference: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graphnet #Deep_Neural_Networks #Clustering
📘 Conference: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
🗓Publish year: 2021
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #Graphnet #Deep_Neural_Networks #Clustering
🎞 Anomaly Detection: Algorithms, Explanations, Applications
💥Free recorded tutorial by Dr. Dietterich’s.He is part of the leadership team for OSU’s Ecosystem Informatics programs including the NSF Summer Institute in Ecoinformatics
💥Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly “alarms” to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Anomaly_Detection #Algorithms #Explanations #Applications
💥Free recorded tutorial by Dr. Dietterich’s.He is part of the leadership team for OSU’s Ecosystem Informatics programs including the NSF Summer Institute in Ecoinformatics
💥Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly “alarms” to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Anomaly_Detection #Algorithms #Explanations #Applications
YouTube
Anomaly Detection: Algorithms, Explanations, Applications
Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly…
📄Current and future directions in network biology
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #biology
🗓Publish year: 2023
📎Study paper
📲Channel: @ComplexNetworkAnalysis
#paper #graph #biology