This week's #ITOps Query episode is out! In the eyes of #CNCF TAG #observability leader Matt Young, #GNNs, #knowledgegraphs and #GraphRAG will boost IT operations and create 3D views of apps and infrastructure. #AI #genai #o11y #aiobservability
https://www.techtarget.com/searchitoperations/podcast/A-visual-future-GraphRAG-and-AI-observability
Dive into the world of #GraphNeuralNetworks (GNNs)!
Discover their advantages over traditional machine learning and have a quick primer on graph representation learning using PyG, a popular open-source GNN library.
🎥 Watch now on #InfoQ: https://bit.ly/3UXutls
#transcript included
Another day, another paper! 📢 Our research paper "A network analysis-based framework to understand the representation dynamics of graph neural networks" has just been published in Neural Computing and Applications (Springer) 🚀
We introduce a unique framework to study Graph Neural Networks (#GNNs) via a network analysis-based framework 🌐. We also have developed a novel training loss function that significantly boosts the GNN's performance! 📈
https://link.springer.com/article/10.1007/s00521-023-09181-w
In this paper, we propose a framework that uses the theory and techniques of (Social) Network Analysis to investigate the learned representations of a Graph Neural Network (GNN, for short). Our framework receives a graph as input and passes it to the GNN to be investigated, which returns suitable node embeddings. These are used to derive insights on the behavior of the GNN through the application of (Social) Network Analysis theory and techniques. The insights thus obtained are employed to define a new training loss function, which takes into account the differences between the graph received as input by the GNN and the one reconstructed from the node embeddings returned by it. This measure is finally used to improve the performance of the GNN. In addition to describe the framework in detail and compare it with related literature, we present an extensive experimental campaign that we conducted to validate the quality of the results obtained.
Asus Zenfone 10 con schermo da 5,9" Amoled, Gorilla Glass Victus, autonomia migliorata, monta lo chip Snapdragon 8 Gen2 che offre un modem Wi-Fi7 incorporato.
We are getting closer to solving the great mystery of #olfaction research:
How to predict the #odour of a molecule based on its chemical structure?
Graphical neural networks (#GNNs) can predict the odour characterisation of a molecule by a group of human experts.
A principal odor map unifies diverse tasks in olfactory perception | Science https://www.science.org/doi/full/10.1126/science.ade4401#F1
📱 Quali sono gli Smartphone con il miglior GPS ?
👉 https://gomoot.com/quale-smartphone-ti-fornisce-la-posizione-gps-piu-precisa/
#smartphone #gps #gnns #dualband #galileo #beidou #doppiafrequenza #glonass #rtx #satellite
Come si è evoluta la tecnologia satellitare sugli Smartphone da GPS a Galileo fino al GNSS e le multi bande e come scegliere uno smartphone con gps preciso
In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent examples include molecules (represented as graphs of atoms and bonds), social networks and transportation networks. This potential has already been seen by key scientific and industrial groups, with already-impacted application areas including traffic forecasting, drug discovery, social network analysis and recommender systems. Further, some of the most successful domains of application for machine learning in previous years -- images, text and speech processing -- can be seen as special cases of graph representation learning, and consequently there has been significant exchange of information between these areas. The main aim of this short survey is to enable the reader to assimilate the key concepts in the area, and position graph representation learning in a proper context with related fields.
Nice intro to Graphs, #GNNs and related #Transformer architectures on the @huggingface blog: https://huggingface.co/blog/intro-graphml
It starts at the very basics about graphs and ends with the application of Transformers and their potential impact on the field.
Hi all,
I'm a PhD student in #MachineLearning at the technical university of Munich #TUM. I'm currently working on machine learning on graphs and machine learning-driven computional chemistry.
#ml #GraphNeuralNetworks #GNNs #compchem
Beth Pariseau
InfoQ
Francesco Cauteruccio
GOMOOT 
NeurophysicsLab Albrecht Haase
Colin Bell 🇨🇦
Johannes
Mircea