ComplEx with N3 Regularizer and Relation Prediction Objective
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ComplEx-N3-RP is a type of machine learning model that is designed to predict relationships between different objects or entities. This…
Laplacian Positional Encodings
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Laplacian Positional Encoding: A Method to Encode Node Positions in a Graph
If you have studied graphs and their applications,…
Hyperboloid Embeddings
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HypE, also known as Hyperboloid Embeddings, is a self-supervised dynamic reasoning framework that creates representations of entities and relations in…
TuckER with Relation Prediction
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TuckER-RP: A Powerful Machine Learning Model for Relation Prediction
TuckER-RP is a machine learning model that is designed to predict…
Wasserstein Embedding for Graph Learning
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WEGL Radio
Overview
WEGL is a student-run radio station at Auburn University in Auburn, Alabama. Founded in 1969, WEGL has…
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation Learning
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DBGAN is a method for graph representation learning that bridges the graph and feature spaces by prototype learning, using a…
RESCAL with Relation Prediction
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Understanding RESCAL-RP
The RESCAL-RP model is a type of machine learning model that is used to help predict relations between…
Spectral Gap Rewiring Layer
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GAP-Layer is a graph neural network layer that helps to optimize the spectral gap of a graph by minimizing or…
CP with N3 Regularizer and Relation Prediction
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CP-N3-RP is a technique used in machine learning to improve the accuracy of predictions. Specifically, it is a combination of…
Commute Times Layer
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Overview of CT-Layer: A Differentiable and Learnable Rewiring Layer
CT-Layer is a graph neural network layer that is able to…
Strain Elevation Tension Spring embedding
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What is SETSe?
SETSe stands for "Simulated Elasticity and Tangential Forces based Spectral Embedding", and it is a deterministic physics-based…
Multi-partition Embedding Interaction
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MEI is a novel approach that addresses the efficiency--expressiveness trade-off issue in knowledge graph embedding, which has been a challenging…
RotatE
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The RotatE model is a powerful method for generating graph embeddings that can model various relation patterns, including symmetry/antisymmetry,…
TuckER
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TuckER is an innovative and state-of-the-art algorithm that is used to perform knowledge graph completion. The algorithm was introduced during…
Graph Isomorphism Network
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Gin has become the latest trend in the world of data science and artificial intelligence. It is an acronym for…
ComplEx with N3 Regularizer
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Overview of ComplEx-N3
ComplEx-N3 is a machine learning model that utilizes a nuclear norm regularizer for training. This model has…
Canonical Tensor Decomposition with N3 Regularizer
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CP-N3: A Hierarchical Tensor Decomposition Method for Data Analysis
CP-N3 is a powerful method for decomposing complex data structures into…
CP with N3 Regularizer
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The topic of CP N3 is a method that is commonly used in order to reduce the complexity of deep…
Large-scale Information Network Embedding
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LINE: An Overview of the Novel Network Embedding Method
In today's world, vast amounts of data are being generated and…
DeepWalk
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DeepWalk is a machine learning method that learns embeddings (social representations) of a graph's vertices. These embeddings capture neighborhood similarity…
RESCAL
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RESCAL RP: An Overview of the Revolutionary Software for Managing Resources and Workforce
What is RESCAL RP?
RESCAL RP is…
VERtex Similarity Embeddings
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VERSE, which stands for VERtex Similarity Embeddings, is a method that creates graph embeddings. These embeddings are specially designed to…
TransE
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TransE is a model used for producing knowledge base embeddings. In simpler terms, knowledge base embeddings can be thought of…
node2vec
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Node2vec is a powerful tool used for learning embeddings for nodes in graphs. In simple terms, node2vec helps to understand…