📃 Explainability in Graph Neural Networks: A Taxonomic Survey

📘 Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence
🗓 Publish year: 2022

🧑‍💻Authors: Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji
🏢University: Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

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📱Channel: @ComplexNetworkAnalysis
#paper #Explainability #GNN #Taxonomic #Survey

📃 Recent advances in manufacturing and processing technologies through graph theoretical approach: A survey

🗓 Publish year: 2023
📘 Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence

🧑‍💻Authors: Parthiban Angamuthu;
Ram Dayal; Samdanielthompson Gabriel; Sathish Kumar Krishnamoorthy; Malaya Ranjan Kar
🏢Universities: Lovely Professional University, Madras Christian College,

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📲Channel: @ComplexNetworkAnalysis
#paper #Survey #manufacturing #processing #technologies

📃 Knowledge Graphs and their Applications in Civil Security

🗓 Publish year: 2020

🧑‍💻Authors: Simon Ott, Daria Liakhovets, Mina Schütz, Medina Andresel, Mihai Bartha, Sven Schlarb, Alexander Schindler
🏢University: Austrian Institute of Technology GmbH
Giefinggasse 4, 1210 Vienna, Austria

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📱Channel: @ComplexNetworkAnalysis
#paper #Knowledge_Graph #Application #Civil_Security

📃Knowledge Graph Embedding: An Overview

🗓 Publish year: 2024
📘 Journal: APSIPA Transactions on Signal and Information Processing (I.F=3.2)

🧑‍💻Authors: Xiou Ge, Yun Cheng Wang, Bin Wang, C.-C. Jay Kuo
🏢Universities: University of Southern California, Institute for Infocomm Research (I2R)

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📲Channel: @ComplexNetworkAnalysis
#paper #Overview #Knowledge_Graph

📃Graph Machine Learning in the Era of Large Language Models (LLMs)

🗓 Publish year: 2023

🧑‍💻Authors: Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li

🏢Universities: The Hong Kong Polytechnic University,Michigan State University, North Carolina State University

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📲Channel: @ComplexNetworkAnalysis
#paper #Graph_Machine_Learning #LLMs

📃Federated Graph Neural Networks: Overview, Techniques, and Challenges

🗓 Publish year: 2024
📘 Journal: IEEE Transactions on Neural Networks and Learning Systems (I.F=14.255)

🧑‍💻Authors: Rui Liu , Pengwei Xing , Zichao Deng, Anran Li , Cuntai Guan , Fellow, IEEE, and Han Yu
🏢Universities: Nanyang Technological University

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📲Channel: @ComplexNetworkAnalysis
#paper #Federated_Graph_Neural_Networks #Challenges #Techniques #Overview

🎞 Machine Learning with Graphs: Pre-Training Graph Neural Networks

💥Free recorded course by Prof. Jure Leskovec

💥There are two challenges in applying GNNs to scientific domains: scarcity of labeled data and out-of-distribution prediction. In this video we discuss methods for pre-training GNNs to resolve these challenges. The key idea is to pre-train both node and graph embeddings, which leads to significant performance gains on downstream tasks.

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📑More details can be found in the paper: Strategies for Pre-training Graph Neural Networks

📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning