63 Repos
Methods for learning vector representations of nodes, edges, and entire graphs.
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Starspace ist ein Vektor-Embedding-Framework, das für das Training hochdimensionaler Repräsentationen von Text und Bildern entwickelt wurde. Es fungiert als Machine-Learning-System für neuronales Ranking, Textklassifizierung und Knowledge-Graph-Embedding und bildet verschiedene Objekttypen in einen gemeinsamen numerischen Raum ab, um Abruf- und Vorhersageaufgaben zu erleichtern. Das System enthält spezialisierte Tools für die Vervollständigung von Knowledge-Graphen und Link-Vorhersagen, indem Entitäten und ihre Beziehungen innerhalb eines multirelationalen Vektorraums dargestellt werden. Es bietet zudem Funktionen für semantische Inhaltsempfehlungen und groß angelegte Textklassifizierung durch Abbildung von Eingaben auf Ziel-Labels oder Kandidatenelemente. Das Framework deckt breite Funktionsbereiche ab, einschließlich ähnlicher Entitäts-Rankings, Vektor-Embedding-Extraktion aus Dokumenten oder N-Grammen und der Verwendung von Random-Walk-basiertem Training. Um große Datensätze zu verwalten, integriert es diskbasiertes komprimiertes Datenladen und Negative-Sampling-Optimierung.
General-purpose embedding framework for various data types.
Generate embeddings from large-scale graph-structured data.
Distributed system for learning embeddings on massive graphs.
DeepWalk - Deep Learning for Graphs
Online learning of social representations using random walks.
This repository provides a reference implementation of node2vec as described in the paper:
Scalable feature learning for networks using random walks.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Comprehensive library of graph embedding and community detection algorithms.
PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"
Learning hierarchical representations using Poincaré embeddings.
Collection of graph embedding techniques and performance benchmarks.
LINE: Large-scale information network embedding
Large-scale information network embedding algorithm.
Semi-supervised learning with graph embeddings
Semi-supervised learning framework using graph embeddings.
Learning distributed representations of entire graphs.
Graph convolutional neural network for multirelational link prediction
Graph neural network for multi-relational link prediction.
A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"
Predicting dynamic embedding trajectories in temporal interaction networks.
Hyperbolic Embeddings
Representation learning using hyperbolic geometry for graph data.
This repository provides a reference implementation of struc2vec.
Learning node representations based on structural identity.
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).
Learning node embeddings via graph attention mechanisms.
A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).
Graph convolutional network designed for signed graphs.
The TensorFlow reference implementation of 'GEMSEC: Graph Embedding with Self Clustering' (ASONAM 2019).
Graph embedding framework with integrated self-clustering.
DSAA 2018 Autoencoders for Link Prediction and Semi-Supervised Node Classification
Autoencoder-based approach for graph prediction tasks.
BiNE: Bipartite Network Embedding
Embedding framework specifically for bipartite network structures.
A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).
Learning node representations that capture multiple social contexts.