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Showing posts older than #953 · Back to latest

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Post #949 795
🎞 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…
  • 👍 2
Post #945 697
📄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
  • 👍 3
Post #944 687
📄Wolfram MathWorld

💥Technical online booklet and workspace

🌐 Study

📲Channel: @ComplexNetworkAnalysis

#online_book #Graph #Graph_Theory
  • 👍 3
Post #943 605
📚 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
  • 👍 5
Post #942 657
📄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
  • 👍 2
  • ❤ 1
  • 👏 1
Post #939 621
📚Graph Theory Notes

🧑‍💼 author: Vadim Lozin in Institute of Mathematics University of Warwick

📎Study

📲Channel: @ComplexNetworkAnalysis
#Booklet #graph
  • 👍 3
Post #938 647
📄Spatial social network research: a bibliometric analysis

📘 journal: Computational Urban Science
🗓Publish year: 2022

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #Spatial #research #bibliometric
  • ❤ 2
Post #936 589
📄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
  • 👍 5
  • 👏 1
Post #935 582
📄 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
  • ❤ 4
  • 👍 1
Post #933 580
📄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
  • 🔥 3
Post #932 568
📄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
  • 👍 3
Post #931 898
📄 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
  • 👍 5
Post #930 544
📄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
  • 👍 3
Post #929 581
📄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
  • 👍 1
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