Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
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DOI: 10.1021/acs.chemmater.9b01294
Abstract: Graph networks are a new machine learning (ML) paradigm that supports both relational reasoning and combinatorial generalization. Here, we develop, for the first time, universal MatErials Graph Network (MEGNet) models for accurate property prediction in… read more here.
Keywords: machine learning; molecules crystals; megnet models; graph networks ... See more keywords
Learning self-driven collective dynamics with graph networks
Sign Up to like & getrecommendations! Published in 2022 at "Scientific Reports"
DOI: 10.1038/s41598-021-04456-5
Abstract: Despite decades of theoretical research, the nature of the self-driven collective motion remains indigestible and controversial, while the phase transition process of its dynamic is a major research issue. Recent methods propose to infer the… read more here.
Keywords: self driven; learning self; graph networks; driven collective ... See more keywords
Explaining Deep Graph Networks via Input Perturbation.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3165618
Abstract: Deep graph networks (DGNs) are a family of machine learning models for structured data which are finding heavy application in life sciences (drug repurposing, molecular property predictions) and on social network data (recommendation systems). The… read more here.
Keywords: deep graph; via input; networks via; explaining deep ... See more keywords
Persistent Homology based Wasserstein distance for graph networks
Sign Up to like & getrecommendations! Published in 2024 at "Hacettepe Journal of Mathematics and Statistics"
DOI: 10.15672/hujms.1317203
Abstract: The technique of measuring similarity between topological spaces using Wasserstein distance between persistence diagrams is extended to graph networks in this paper. A relationship between the Wasserstein distance of the Cartesian product of topological spaces… read more here.
Keywords: distance; product; wasserstein distance; graph networks ... See more keywords
Uniform Pooling for Graph Networks
Sign Up to like & getrecommendations! Published in 2020 at "Applied Sciences"
DOI: 10.3390/app10186287
Abstract: The graph convolution network has received a lot of attention because it extends the convolution to non-Euclidean domains. However, the graph pooling method is still less concerned, which can learn coarse graph embedding to facilitate… read more here.
Keywords: pooling graph; uniform pooling; graph; convolution ... See more keywords
Applying Named Entity Recognition and Graph Networks to Extract Common Interests from Thematic Subfora on Reddit
Sign Up to like & getrecommendations! Published in 2024 at "Applied Sciences"
DOI: 10.3390/app14051696
Abstract: Reddit is the largest topically structured social network. Existing literature, reporting results of Reddit-related research, considers different phenomena, from social and political studies to recommender systems. The most common techniques used in these works, include… read more here.
Keywords: graph networks; named entity; reddit; entity recognition ... See more keywords
Discourse-Aware Graph Networks for Textual Logical Reasoning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on pattern analysis and machine intelligence"
DOI: 10.48550/arxiv.2207.01450
Abstract: Textual logical reasoning, especially question-answering (QA) tasks with logical reasoning, requires awareness of particular logical structures. The passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence). However, such structures are… read more here.
Keywords: discourse aware; aware graph; logical reasoning; textual logical ... See more keywords