CP-N3: A Hierarchical Tensor Decomposition Method for Data Analysis
CP-N3 is a powerful method for decomposing complex data structures into their component parts. This technique uses a mathematical tool called a tensor to represent complex data sets, and then applies a decomposition algorithm to obtain a set of simpler, more manageable representations. In particular, CP-N3 uses a canonical tensor decomposition method that is trained using a regularized variant of the N3 regularization technique
Overview of CT-Layer: A Differentiable and Learnable Rewiring Layer
CT-Layer is a graph neural network layer that is able to rewire a graph in an inductive and parameter-free way according to the commute times distance or effective resistance. CT-Layer addresses the issue of learning a differentiable way to compute the CT-embedding of the graph, which is not possible with the traditional spectral version. CT-Layer provides a new approach to rewire a given graph optimally, leading to a better un
ComplEx-N3-RP is a type of machine learning model that is designed to predict relationships between different objects or entities. This type of model is used in a wide range of applications, including natural language processing, social network analysis, and recommendation systems.
What is ComplEx?
ComplEx, which stands for Complex-valued Embedding of Entities and Relations, is a type of neural network that is designed to represent objects and relationships in a complex vector space. This mea
Overview of ComplEx-N3
ComplEx-N3 is a machine learning model that utilizes a nuclear norm regularizer for training. This model has several applications in natural language processing, information retrieval, and knowledge representation. It is considered as one of the state-of-the-art models for knowledge graph embedding.
What is ComplEx-N3?
ComplEx-N3 is a complex-valued neural network that can learn feature representations for entities and relationships in a knowledge graph. A knowledge gr
CP-N3-RP is a technique used in machine learning to improve the accuracy of predictions. Specifically, it is a combination of two strategies: a regularizer and a relation predictor.
What is a Regularizer?
A regularizer is simply a mathematical formula applied to a set of data in order to simplify it. In machine learning, it is used to prevent overfitting, which is a problem that occurs when a model is too complex and becomes too narrowly focused on the training data. This can lead to poor per
The topic of CP N3 is a method that is commonly used in order to reduce the complexity of deep learning models in artificial intelligence. In particular, it focuses on using a mathematical regularization technique known as the N3 regularizer.
What is CP N3?
CP N3 stands for Canonical Polyadic decomposition with N3 regularization. To understand what this means, first it is important to know what polyadic decomposition is. Polyadic decomposition is a technique used in linear algebra that breaks
DeepWalk is a machine learning method that learns embeddings (social representations) of a graph's vertices. These embeddings capture neighborhood similarity and community membership by encoding social relations in a continuous vector space with a relatively small number of dimensions.
The Goal of DeepWalk
The main goal of DeepWalk is to learn a latent representation, not only a probability distribution of node co-occurrences. This is achieved by introducing a mapping function $\Phi \colon v
DBGAN is a method for graph representation learning that bridges the graph and feature spaces by prototype learning, using a structure-aware approach to estimate the prior distribution of latent representation. This approach is different from the more commonly used normal distribution assumption.
What is Graph Representation Learning?
Graph representation learning is an area of machine learning concerned with generating numerical representations of graphs or networks. Graphs are important for
Gin has become the latest trend in the world of data science and artificial intelligence. It is an acronym for Graph Isomorphism Network, and it has been generating a lot of buzz in the scientific community. This algorithm has been hailed as being one of the most discriminative GNNs available, as it utilizes a process known as the WL test.
What is Gin?
Gin, which stands for Graph Isomorphism Network, is a new machine learning algorithm designed for data scientists, artificial intelligence sys
HypE, also known as Hyperboloid Embeddings, is a self-supervised dynamic reasoning framework that creates representations of entities and relations in a Knowledge Graph (KG). By utilizing positive first-order existential queries, HypE can learn these representations as hyperboloids in a Poincaré ball.
How HypE Works
The queries used by HypE are translated geometrically as translation (t), intersection ($\cap$), and union ($\cup$) and the result is a model that significantly outperforms existi
Laplacian Positional Encoding: A Method to Encode Node Positions in a Graph
If you have studied graphs and their applications, you may have heard about Laplacian eigenvectors. These eigenvectors are a natural generalization of the Transformer positional encodings (PE) for graphs, and they help encode distance-aware information in a graph. Laplacian positional encoding is a general method to encode node positions in a graph using these eigenvectors.
What are Laplacian Eigenvectors?
Before und
LINE: An Overview of the Novel Network Embedding Method
In today's world, vast amounts of data are being generated and collected every second. Understanding this data can help in various fields, including social network analysis, recommendation systems, and machine learning. However, this data is often in the form of a network, which can be challenging to analyze.
LINE, short for "Large-scale Information Network Embedding," is a novel network embedding method developed by Tang et al. in 2015.
MEI is a novel approach that addresses the efficiency--expressiveness trade-off issue in knowledge graph embedding, which has been a challenging task in machine learning. This technique uses the *multi-partition embedding interaction* with block term tensor format to separate the embedding vectors into multiple partitions and learn the local interaction patterns from the data. This way, MEI is able to achieve the optimal balance between efficiency and expressiveness, rather than being exclusivel
Node2vec is a powerful tool used for learning embeddings for nodes in graphs. In simple terms, node2vec helps to understand how different nodes in a graph are related to each other.
What is node2vec?
Node2vec is a machine learning algorithm used for generating embeddings, or a concise numerical representation, of nodes in graphs. With the help of node2vec, researchers can analyze and understand how different nodes relate to each other in a graph.
Node2vec maximizes a likelihood objective ove
Understanding RESCAL-RP
The RESCAL-RP model is a type of machine learning model that is used to help predict relations between different entities in a dataset. It is based on the RESCAL model, which stands for Restricted Boltzmann Machines Entity-Entity Relation. Essentially, the RESCAL model is a way to represent entities and their relationships in a mathematical format, making it easier to analyze and work with large sets of data. The RESCAL-RP model builds on this by adding a relation predic
RESCAL RP: An Overview of the Revolutionary Software for Managing Resources and Workforce
What is RESCAL RP?
RESCAL RP is a cutting-edge technology that has revolutionized the way resources and workforce are managed. It is an online software that makes it possible to manage and optimize resources in real-time, in a way that has never been done before.
With the RESCAL RP platform, companies can easily and efficiently allocate their resources, including people, equipment, and facilities. By do
The RotatE model is a powerful method for generating graph embeddings that can model various relation patterns, including symmetry/antisymmetry, inversion, and composition. It defines each relation as a rotation from the source entity to the target entity in the complex vector space. The RotatE model is trained using a self-adversarial negative sampling technique.
What is RotatE?
RotatE is a method for generating graph embeddings, which capture the essential features of a graph, such as its t
GAP-Layer is a graph neural network layer that helps to optimize the spectral gap of a graph by minimizing or maximizing the bottleneck size. The goal of GAP-Layer is to create more connected or separated communities depending on the mining task required.
The Spectral Gap Rewiring
The first step in implementing GAP-Layer is to minimize the spectral gap by minimizing the loss function. The loss function is given by:
$$ L\_{Fiedler} = \|\tilde{\mathbf{A}}-\mathbf{A}\| \_F + \alpha(\lambda\_2)^