📃 Understanding Graph Embedding Methods and Their Applications
📗 Journal: Society for Industrial and Applied Mathematic (I.F=1.698)
🗓 Publish year: 2021
🧑💻Authors: Mengjia Xu
🏢Universities: Massachusetts Institute of Technology
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Applications #graph_Embedding
📗 Journal: Society for Industrial and Applied Mathematic (I.F=1.698)
🗓 Publish year: 2021
🧑💻Authors: Mengjia Xu
🏢Universities: Massachusetts Institute of Technology
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Applications #graph_Embedding
👍2
📃 Network analytics: an introduction and illustrative applications in health data science
📘 Journal: Journal of Information Technology Case and Application Research
🗓 Publish year: 2023
🧑💻Authors: Pankush Kalgotra, Ramesh Sharda
🏢Universities: Auburn University, Oklahoma State University
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #applications #health #data_science
📘 Journal: Journal of Information Technology Case and Application Research
🗓 Publish year: 2023
🧑💻Authors: Pankush Kalgotra, Ramesh Sharda
🏢Universities: Auburn University, Oklahoma State University
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #applications #health #data_science
👍2❤1
📃 A survey on bipartite graphs embedding
📗 Journal: Social Network Analysis and Mining (I.F=2.8)
🗓 Publish year: 2023
🧑💻Authors: Edward Giamphy, Jean‑Loup Guillaume, Antoine Doucet, Kevin Sanchis
🏢Universities: La Rochelle University
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #bipartite #graph_Embedding #survey
📗 Journal: Social Network Analysis and Mining (I.F=2.8)
🗓 Publish year: 2023
🧑💻Authors: Edward Giamphy, Jean‑Loup Guillaume, Antoine Doucet, Kevin Sanchis
🏢Universities: La Rochelle University
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #bipartite #graph_Embedding #survey
👍4
📃 A Literature Review of Recent Graph Embedding Techniques for Biomedical Data
📘Conference: International Conference on Neural Information Processing
🗓 Publish year: 2021
🧑💻Authors: Yankai Chen, Yaozu Wu, Shicheng Ma, Irwin King
🏢University: The Chinese University of Hong Kong
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #Biomedical #review
📘Conference: International Conference on Neural Information Processing
🗓 Publish year: 2021
🧑💻Authors: Yankai Chen, Yaozu Wu, Shicheng Ma, Irwin King
🏢University: The Chinese University of Hong Kong
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #Graph_Embedding #Biomedical #review
👍5
📃 A Systematic Review of Deep Graph Neural Networks: Challenges, Classification, Architectures, Applications & Potential Utility in Bioinformatics
📗 Journal: Social Network Analysis and Mining (I.F=2.8)
🗓 Publish year: 2023
🧑💻Authors: Mudasir Malla, Adil ; Banka, Asif Ali
🏢Universities: Islamic University of Science & Technology
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Bioinformatics #Deep_GNN #Review
📗 Journal: Social Network Analysis and Mining (I.F=2.8)
🗓 Publish year: 2023
🧑💻Authors: Mudasir Malla, Adil ; Banka, Asif Ali
🏢Universities: Islamic University of Science & Technology
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Bioinformatics #Deep_GNN #Review
👍3👏1
📃 Graph neural networks for clinical risk prediction based on electronic health records: A survey
📘 Journal: Journal of Biomedical Informatics (I.F=4.5)
🗓 Publish year: 2024
🧑💻Authors: Heloísa Oss Boll, Ali Amirahmadi, Mirfarid Musavian Ghazani, Wagner Ourique de Morais, Edison Pignaton de Freitas, Amira Soliman, Farzaneh Etminani, Stefan Byttner, Mariana Recamonde-Mendoza
🏢Universities: Universidade Federal do Rio Grande do Sul, Halmstad University
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #risk #prediction #electronic #health #survey
📘 Journal: Journal of Biomedical Informatics (I.F=4.5)
🗓 Publish year: 2024
🧑💻Authors: Heloísa Oss Boll, Ali Amirahmadi, Mirfarid Musavian Ghazani, Wagner Ourique de Morais, Edison Pignaton de Freitas, Amira Soliman, Farzaneh Etminani, Stefan Byttner, Mariana Recamonde-Mendoza
🏢Universities: Universidade Federal do Rio Grande do Sul, Halmstad University
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #GNN #risk #prediction #electronic #health #survey
👍3
🎞 Machine Learning with Graphs: Applications of Deep Graph Generation.
💥Free recorded course by Jure Leskovec, Computer Science, PhD
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
💥Free recorded course by Jure Leskovec, Computer Science, PhD
📽 Watch
📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #Machine_Learning #DGNN #GNN
YouTube
Stanford CS224W: ML with Graphs | 2021 | Lecture 15.4 - Applications of Deep Graph Generation
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3EwmakW
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finally…
Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation
Jure Leskovec
Computer Science, PhD
Finally…
👍1
📃 A Bibliometric Analysis of Recent Developments and Trends in Knowledge Graph Research (2013–2022)
📗 Journal: IEEE ACCESS (I.F=3.9)
🗓 Publish year: 2024
🧑💻Authors: GANG WANG, JING HE
🏢Universities: Chaohu University
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Bibliometric #Knowledge_Graph
📗 Journal: IEEE ACCESS (I.F=3.9)
🗓 Publish year: 2024
🧑💻Authors: GANG WANG, JING HE
🏢Universities: Chaohu University
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Bibliometric #Knowledge_Graph
👍4
🎞 Machine Learning with Graphs - Node Embeddings
💥SDML is partnering with Houston Machine Learning on a series about machine learning with graphs.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Machine_Learning #Graph #Node_Embedding
💥SDML is partnering with Houston Machine Learning on a series about machine learning with graphs.
📽 Watch
📱Channel: @ComplexNetworkAnalysis
#video #Machine_Learning #Graph #Node_Embedding
YouTube
Machine Learning with Graphs - Node Embeddings
SDML is partnering with Houston Machine Learning on a series about machine learning with graphs. The content will be mainly based on the Stanford course: http://web.stanford.edu/class/cs224w/
Series schedule:
Introduction; Machine Learning for Graphs
Traditional…
Series schedule:
Introduction; Machine Learning for Graphs
Traditional…
👍3👏2
📃 Artificial Intelligence for Complex Network: Potential, Methodology and Application
🗓 Publish year: 2024
🧑💻Authors: Jingtao Ding, Chang Liu, Yu Zheng, Yunke Zhang, Zihan Yu, Ruikun Li, Hongyi Chen, Jinghua Piao, Huandong Wang, Jiazhen Liu, Yong Li
🏢University: Tsinghua University
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #Artificial_Intelligence #Potential #Methodology #Application
🗓 Publish year: 2024
🧑💻Authors: Jingtao Ding, Chang Liu, Yu Zheng, Yunke Zhang, Zihan Yu, Ruikun Li, Hongyi Chen, Jinghua Piao, Huandong Wang, Jiazhen Liu, Yong Li
🏢University: Tsinghua University
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #Artificial_Intelligence #Potential #Methodology #Application
👍3🔥2
📄Stanford Network Analysis Platform (SNAP)
💥Purpose:
SNAP is a general-purpose network analysis and graph mining library.
🔹Language: It is written in C++.
🔹Scalability: SNAP easily scales to handle massive networks with hundreds of millions of nodes and billions of edges.
💥Functionality:
Efficiently manipulates large graphs.
Calculates structural properties.
Generates regular and random graphs.
Supports attributes on nodes and edges.
🔹Python Interface: Snap.py provides a Python interface for SNAP, combining the performance benefits of SNAP with the flexibility of Python.
💥Stanford Large Network Dataset Collection:
This collection includes over 50 large network datasets:
🔹Social networks: Represent online social interactions between people.
🔹Networks with ground-truth communities: These are community structures in social and information networks.
🔹Communication networks: Email communication networks, where edges represent communication between individuals.
💥Tutorials and Recent Events:
SNAP hosts tutorials on topics such as deep learning for network biology, representation learning on networks, and more.
They have organized workshops and tutorials at conferences like ISMB, The Web Conference, and WWW.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #Python #Tutorials #Dataset
💥Purpose:
SNAP is a general-purpose network analysis and graph mining library.
🔹Language: It is written in C++.
🔹Scalability: SNAP easily scales to handle massive networks with hundreds of millions of nodes and billions of edges.
💥Functionality:
Efficiently manipulates large graphs.
Calculates structural properties.
Generates regular and random graphs.
Supports attributes on nodes and edges.
🔹Python Interface: Snap.py provides a Python interface for SNAP, combining the performance benefits of SNAP with the flexibility of Python.
💥Stanford Large Network Dataset Collection:
This collection includes over 50 large network datasets:
🔹Social networks: Represent online social interactions between people.
🔹Networks with ground-truth communities: These are community structures in social and information networks.
🔹Communication networks: Email communication networks, where edges represent communication between individuals.
💥Tutorials and Recent Events:
SNAP hosts tutorials on topics such as deep learning for network biology, representation learning on networks, and more.
They have organized workshops and tutorials at conferences like ISMB, The Web Conference, and WWW.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #Python #Tutorials #Dataset
🔥2👏2👍1
📃 A Survey on Temporal Knowledge Graph: Representation Learning and Applications
🗓 Publish year: 2024
🧑💻Authors: JLi Cai, Xin Mao, Yuhao Zhou, Zhaoguang Long, Changxu Wu, Man Lan
🏢Universities: East China Nomal University, Guizhou University, Tsinghua University
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #Temporal #Knowledge_Graph #Representation_Learning #Application #survey
🗓 Publish year: 2024
🧑💻Authors: JLi Cai, Xin Mao, Yuhao Zhou, Zhaoguang Long, Changxu Wu, Man Lan
🏢Universities: East China Nomal University, Guizhou University, Tsinghua University
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #Temporal #Knowledge_Graph #Representation_Learning #Application #survey
👍3👏2
📃 Higher-Order Networks Representation and Learning: A Survey
🗓 Publish year: 2024
🧑💻Authors: Hao Tian and Reza Zafarani
🏢Universities: Syracuse University
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Higher_Order #Survey
🗓 Publish year: 2024
🧑💻Authors: Hao Tian and Reza Zafarani
🏢Universities: Syracuse University
📎 Study the paper
📲Channel: @ComplexNetworkAnalysis
#paper #Higher_Order #Survey
👍6
📄Data Mining Graphs and Networks
💥Technical Paper
💥Graph mining is a process in which the mining techniques are used in finding a pattern or relationship in the given real-world collection of graphs. By mining the graph, frequent substructures and relationships can be identified which helps in clustering the graph sets, finding a relationship between graph sets, or discriminating or characterizing graphs. Predicting these patterning trends can help in building models for the enhancement of any application that is used in real-time. To implement the process of graph mining, one must learn to mine frequent subgraphs.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code
💥Technical Paper
💥Graph mining is a process in which the mining techniques are used in finding a pattern or relationship in the given real-world collection of graphs. By mining the graph, frequent substructures and relationships can be identified which helps in clustering the graph sets, finding a relationship between graph sets, or discriminating or characterizing graphs. Predicting these patterning trends can help in building models for the enhancement of any application that is used in real-time. To implement the process of graph mining, one must learn to mine frequent subgraphs.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code
GeeksforGeeks
Data Mining Graphs and Networks - GeeksforGeeks
A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.
👍7
📃 Link Prediction Using Graph Neural Networks for Recommendation Systems
📘 Journal: Procedia Computer Science
🗓 Publish year: 2023
🧑💻Authors: Hmaidi Safae, Lazaar Mohamed , Abdellah Chehri , El Madani El Alami Yasser , Rachid Saadane
🏢Universities: University in Rabat, Rabat, Morocco, Royal Military College of Canada
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #GNN #Recommender_Systems
📘 Journal: Procedia Computer Science
🗓 Publish year: 2023
🧑💻Authors: Hmaidi Safae, Lazaar Mohamed , Abdellah Chehri , El Madani El Alami Yasser , Rachid Saadane
🏢Universities: University in Rabat, Rabat, Morocco, Royal Military College of Canada
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #GNN #Recommender_Systems
👍7
📄Intro to Gephi & Visualize clusters
💥Goals:
-Learn how to use Gephi
-Explore a directed network
-Export a network map
-Annotate clusters
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Gephi
💥Goals:
-Learn how to use Gephi
-Explore a directed network
-Export a network map
-Annotate clusters
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Gephi
👍8
📃 Progress on network modeling and analysis of gut microecology: a review
📘 Journal: Applied and Environmental Microbiology (I.F=4.4)
🗓 Publish year: 2024
🧑💻Authors: Meng Luo, Jinlin Zhu, Jiajia Jia, Hao Zhang, Jianxin Zhao
🏢University: Jiangnan University
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #Progress #gut #microecology #review
📘 Journal: Applied and Environmental Microbiology (I.F=4.4)
🗓 Publish year: 2024
🧑💻Authors: Meng Luo, Jinlin Zhu, Jiajia Jia, Hao Zhang, Jianxin Zhao
🏢University: Jiangnan University
📎 Study the paper
📱Channel: @ComplexNetworkAnalysis
#paper #Progress #gut #microecology #review
👍3
📄The Essential Guide to GNN (Graph Neural Networks)
💥Technical Paper
💥 Graph neural networks (GNNs) are a set of deep learning methods that work in the graph domain. These networks have recently been applied in multiple areas including; combinatorial optimization, recommender systems, computer vision – just to mention a few. These networks can also be used to model large systems such as social networks, protein-protein interaction networks, knowledge graphs among other research areas. Unlike other data such as images, graph data works in the non-euclidean space. Graph analysis is therefore aimed at node classification, link prediction, and clustering.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #GNN
💥Technical Paper
💥 Graph neural networks (GNNs) are a set of deep learning methods that work in the graph domain. These networks have recently been applied in multiple areas including; combinatorial optimization, recommender systems, computer vision – just to mention a few. These networks can also be used to model large systems such as social networks, protein-protein interaction networks, knowledge graphs among other research areas. Unlike other data such as images, graph data works in the non-euclidean space. Graph analysis is therefore aimed at node classification, link prediction, and clustering.
🌐 Study
📲Channel: @ComplexNetworkAnalysis
#paper #Graph #code #GNN
cnvrg
The Essential Guide to GNN (Graph Neural Networks) | Intel® Tiber™ AI Studio
Graph neural networks (GNNs) are a set of deep learning methods that work in the graph domain. These networks have recently been applied in multiple areas
👍5