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Showing posts older than #1087 · Back to latest

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Post #1086 908
📃 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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📱Channel: @ComplexNetworkAnalysis
#paper #Recommender_Systems #Education #review
  • 👍 3
Post #1083 854
A_review_on_graph_based_approaches_for_network_security_monitoring.pdf1.1 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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  • 👍 1
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Post #1082 5.11K
📃 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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📱Channel: @ComplexNetworkAnalysis
#paper #GNN #predicting #synergistic #drug_combinations #review
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Post #1081 849
📃 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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📲Channel: @ComplexNetworkAnalysis
#paper #Recommendation_Systems #Review
  • 👍 4
Post #1080 1.54K
📄What Are Graph Neural Networks? How GNNs Work, Explained with Examples

💥Technical Paper

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📲Channel: @ComplexNetworkAnalysis

#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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Post #1079 942
📄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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📲Channel: @ComplexNetworkAnalysis

#paper #Graph #code #GNN
  • 👍 5
Post #1078 812
📃 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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📱Channel: @ComplexNetworkAnalysis
#paper #Progress #gut #microecology #review
  • 👍 3
Post #1077 1.31K
📄Intro to Gephi & Visualize clusters

💥Goals:
-Learn how to use Gephi
-Explore a directed network
-Export a network map
-Annotate clusters

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📲Channel: @ComplexNetworkAnalysis

#paper #Gephi
  • 👍 8
Post #1074 959
📃 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

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📱Channel: @ComplexNetworkAnalysis
#paper #Link_Prediction #GNN #Recommender_Systems
  • 👍 7
Post #1073 1.13K
📄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.

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📲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
Post #1072 911
📃 Higher-Order Networks Representation and Learning: A Survey

🗓 Publish year: 2024

🧑‍💻Authors: Hao Tian and Reza Zafarani
🏢Universities: Syracuse University

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📲Channel: @ComplexNetworkAnalysis
#paper #Higher_Order #Survey
  • 👍 6
Post #1071 1.01K
📃 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

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📱Channel: @ComplexNetworkAnalysis
#paper #Temporal #Knowledge_Graph #Representation_Learning #Application #survey
  • 👍 3
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Post #1070 968
📄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.

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📲Channel: @ComplexNetworkAnalysis

#paper #Graph #code #Python #Tutorials #Dataset
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Post #1069 1.43K
📃 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

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📱Channel: @ComplexNetworkAnalysis
#paper #Artificial_Intelligence #Potential #Methodology #Application
  • 👍 3
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Post #1068 976
🎞 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
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…
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Post #1065 1.49K
📃 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

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📲Channel: @ComplexNetworkAnalysis
#paper #Bibliometric #Knowledge_Graph
  • 👍 4
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