Network Analysis Resources & Updates
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Network Analysis Resources & Updates
3.09K subscribers
Network Analysis Resources & Updates
📃 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

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#paper #Progress #gut #microecology #review
👍3
794 views
Network Analysis Resources & Updates
📄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.

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#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
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921 viewsedited  
Network Analysis Resources & Updates
📄What Are Graph Neural Networks? How GNNs Work, Explained with Examples

💥Technical Paper

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#paper #Graph #code #GNN #python
freeCodeCamp.org
What Are Graph Neural Networks? How GNNs Work, Explained with Examples
By Rishit Dagli Graph Neural Networks are getting more and more popular and are being used extensively in a wide variety of projects. In this article, I help you get started and understand how graph neural networks work while also trying to address t...
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Network Analysis Resources & Updates
📃 Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

📘 Journal: Electronics (I.F=2.9)
🗓 Publish year: 2022

🧑‍💻Authors: Sadaf Safavi ,Mehrdad Jalali ,Mahboobeh Houshmand
🏢Universities: Islamic Azad University, Karlsruhe Institute of Technology

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#paper #Recommendation_Systems #Review
👍4
843 viewsedited  
Network Analysis Resources & Updates
📃 A review on graph neural networks for predicting synergistic drug combinations

📘 Journal: Artificial Intelligence Review (I.F=12)
🗓 Publish year: 2024

🧑‍💻Authors: Milad Besharatifard, Fatemeh Vafaee
🏢University: University of New South Wales (UNSW), Sydney, Australia

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#paper #GNN #predicting #synergistic #drug_combinations #review
👍5❤1👏1
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Network Analysis Resources & Updates
A_review_on_graph_based_approaches_for_network_security_monitoring.pdf
1.1 MB
📃 A review on graph-based approaches for network security monitoring and botnet detection

📘 Journal: International Journal of Information Security (I.F=3.2)
🗓 Publish year: 2024

🧑‍💻Authors: Sofiane Lagraa, Martin Husák, Hamida Seba, Satyanarayana Vuppala, Radu State & Moussa Ouedraogo
🏢Universities: University of Luxembourg,Masaryk University

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#paper #network_security_monitoring #botnet_detection #Review
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846 viewsedited  
Network Analysis Resources & Updates
📄Introducing TensorFlow Graph Neural Networks

💥Technical Paper

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#paper #Graph #code #TensorFlow #python
blog.tensorflow.org
Introducing TensorFlow Graph Neural Networks
Introducing TensorFlow GNN, a library to build Graph Neural Networks on the TensorFlow
platform.
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1.35K viewsedited  
Network Analysis Resources & Updates
📄Graph-Based Data Science, Machine Learning, and AI

💥Technical Paper

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#paper #Graph #AI #Data_Science #Machine_Learning
DZone
Graph-Based Data Science, Machine Learning, and AI
What does graphing have to do with machine learning and data science? A lot, actually — learn more in The Year of the Graph Newsletter's Spring 2021 edition.
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1.35K views
Network Analysis Resources & Updates
📃 Recommendation Systems for Education: Systematic Review

📘 Journal: Electronics (I.F=2.9)
🗓 Publish year: 2021

🧑‍💻Authors: María Cora Urdaneta-Ponte, Amaia Mendez-Zorrilla, Ibon Oleagordia-Ruiz
🏢Universities: University of Deusto, Andres Bello Catholic University (UCAB)

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#paper #Recommender_Systems #Education #review
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Network Analysis Resources & Updates
🔊 Important Reminder:

💥 Deadline Approaching for

📓 "Advances in Graph-Based Data Mining" Special Issue

🔶Topics:
▫️graph-based data mining
▫️network analysis
▫️graph algorithms
▫️graph neural networks
▫️community detection
▫️complex data relationships
▫️knowledge extraction

🌐 More information & Submission

📲Channel: @ComplexNetworkAnalysis
#journal #special_issue
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853 views
Network Analysis Resources & Updates
📃 A social network of crime: A review of the use of social networks for crime and the detection of crime

📘 Journal: Online Social Networks and Media (I.F=7.61)
🗓 Publish year: 2024

🧑‍💻Authors: Brett Drury, Samuel Morais Drury, Md Arafatur Rahman, Ihsan Ullah
🏢Universities: National University of Ireland Galway, University College Dublin, Liverpool Hope University, University Malaysia Pahang

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#paper #crime #social_network #Review
👍4
990 views
Network Analysis Resources & Updates
📃 Social search: Retrieving information in Online Social platforms – A survey

📘 Journal: Online Social Networks and Media
🗓 Publish year: 2023

🧑‍💻Authors: Maddalena Amendola, Andrea Passarella, Raffaele Perego
🏢University: University of Pisa

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#paper #Social #Retrieving_information #survey
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Network Analysis Resources & Updates
🎞 Machine Learning with Graphs: Graph Neural Networks in Computational Biology

💥Free recorded course by Prof. Marinka Zitnik

💥In this lecture, Prof. Marinka gives an overview of why graph learning techniques can greatly help with computational biology research. Concretely, this talk covers 3 exemplar use cases: (1) Discovering safe drug-drug combinations via multi-relational link prediction on heterogenous knowledge graphs; (2) Classify patient outcomes and diseases via learning subgraph embeddings; and (3) Learning effective disease treatments through few-shot learning for graphs.

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#video #course #Graph #GNN #Machine_Learning #computational_biology
YouTube
Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 18 - GNNs in Computational Biology
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2XVImFC

Lecture 18 - Graph Neural Networks in Computational Biology
Jure Leskovec
Computer Science, PhD

We are glad to invite Prof.…
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6.51K viewsedited  
Network Analysis Resources & Updates
🎓Study of Tensor Network Applications in Complex Networks

📘Integrated master's thesis in engineering physics

🗓Publish year: 2022

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#Thesis #Tensor_Networks #Application
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874 views
Network Analysis Resources & Updates
📃Data-centric Graph Learning: A Survey

📘 Journal: JOURNAL OF LATEX CLASS FILES
🗓 Publish year: 2021

🧑‍💻Authors: Yuxin Guo, Deyu Bo, Cheng Yang, Zhiyuan Lu, Zhongjian Zhang, Jixi Liu, Yufei Peng, Chuan Shi
🏢Universities: Beijing University of Posts and Telecommunications

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#paper #crime #Graph_Learning #Survey
👏2
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Network Analysis Resources & Updates
📃Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction

📘 Journal: Briefings in Bioinformatics(I.F=13.994)
🗓 Publish year: 2023

🧑‍💻Authors: Xuan Lin, Lichang Dai, Yafang Zhou, Zu-Guo Yu, Wen Zhang, Jian-Yu Shi, Dong-Sheng Cao, Li Zeng, Haowen Chen, Bosheng Song, Philip S Yu, Xiangxiang Zeng
🏢Universities: Xiangtan University, Huazhong Agricultural University, Hunan University,

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#paper #drug_drug_interactions #Graph_Learning #deep_learning #prediction
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869 viewsedited  
Network Analysis Resources & Updates
📃 A review of Graph Neural Networks for Electroencephalography data analysis

📘 Journal: Neurocomputing (I.F=6)
🗓 Publish year: 2023

🧑‍💻Authors: Manuel Graña, Igone Morais-Quilez
🏢University: University of the Basque Country (UPV/EHU), San Sebastian, Spain

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#paper #GNN #Electroencephalography #review
👍2
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Network Analysis Resources & Updates
📕Handbook on Biological networks

✨Networks at the Cellular Level
-The Structural Network Properties of Biological Systems (M Brilli & P Lió)
-Dynamics of Multicellular Synthetic Gene Networks (E Ullner et al.)
-Boolean Networks in Inference and Dynamic Modeling of Biological Systems at the Molecular and Physiological Level (J Thakar & R Albert)
-Complexity of Boolean Dynamics in Simple Models of Signaling Networks and in Real Genetic Networks (A Díaz-Guilera & R Álvarez-Buylla)
-Geometry and Topology of Folding Landscapes (L Bongini & L Casetti)
-Elastic Network Models for Biomolecular Dynamics: Theory and Application to Membrane Proteins and Viruses (T R Lezon et al.)
-Metabolic Networks (M C Palumbo et al.)
✨Brain Networks:
-The Human
Brain Network (O Sporns)
-Brain Network Analysis from High-Resolution EEG Signals (F De Vico Fallani & F Babiloni)
-An Optimization Approach to the Structure of the Neuronal layout of C elegans (A Arenas et al.)
-Cultured Neuronal Networks Express Complex Patterns of Activity and Morphological Memory (N Raichman et al.)
-Synchrony and Precise Timing in Complex Neural Networks (R-M Memmesheimer & M Timme)
✨Networks at the Individual and Population Levels:
-Ideas for Moving Beyond Structure to Dynamics of Ecological Networks (D B Stouffer et al.)
-Evolutionary Models for Simple Biosystems (F Bagnoli)
-Evolution of Cooperation in Adaptive Social Networks (S Van Segbroeck et al.)
-From Animal Collectives and Complex Networks to Decentralized Motion Control Strategies (A Buscarino et al.)
-Interplay of Network State and Topology in Epidemic Dynamics (T Gross)

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#Handbook #Biological
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908 views
Network Analysis Resources & Updates
📃Multilayer Clustered Graph Learning

🗓 Publish year: 2020

🧑‍💻Authors: Mireille El Gheche, Pascal Frossard
🏢Universities: Ecole Polytechnique Fed´ erale de Lausanne (EPFL)

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#paper #Graph_Learning #Multilayer_graph
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