• t-SNE

  • Referenced in 176 articles [sw22300]
  • technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much ... that reveals structure at many different scales. This is particularly important for high-dimensional data ... random walks on neighborhood graphs to allow the implicit structure of all of the data ... including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced...
  • PyTorch-BigGraph

  • Referenced in 4 articles [sw34086]
  • PyTorch-BigGraph: A Large-scale Graph Embedding System. Graph embedding methods produce unsupervised node features ... traditional multi-relation embedding systems that allow it to scale to graphs with billions ... edges. PBG uses graph partitioning to train arbitrarily large embeddings on either a single machine ... existing embedding systems on common benchmarks, while allowing for scaling to arbitrarily large graphs...
  • DGL-KE

  • Referenced in 3 articles [sw34088]
  • Training Knowledge Graph Embeddings at Scale. Knowledge graphs (KGs) are data structures that store information ... learning tasks is to compute knowledge graph embeddings. DGL-KE is a high performance, easy ... scalable package for learning large-scale knowledge graph embeddings. The package is implemented...
  • GraphVite

  • Referenced in 2 articles [sw34087]
  • high speed and large scale. GraphVite is a general graph embedding engine, dedicated to high ... speed and large-scale embedding learning in various applications. GraphVite provides complete training and evaluation ... pipelines for 3 applications: node embedding, knowledge graph embedding and graph & high-dimensional data visualization...
  • OpenKE

  • Referenced in 5 articles [sw30611]
  • embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs ... support quick model validation and large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity ... toolkit, the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE ... answering. The toolkit, documentation, and pre-trained embeddings are all released on http://openke.thunlp.org...
  • HyPy

  • Referenced in 2 articles [sw40557]
  • graph representations, there is significant interest in scalable hyperbolic-space embedding methods. These embeddings enable ... existing landmark-based hyperbolic embedding algorithms for large-scale graphs. Whereas previous methods compute...
  • AnnexML

  • Referenced in 5 articles [sw30153]
  • methods have been widely used in Web-scale classification tasks such as Web page tagging ... this paper, we present a novel graph embedding method called ”AnnexML”. At the training step ... neighbor graph in the embedding space. We conducted evaluations on several large-scale real-world...
  • SpectralNet

  • Referenced in 6 articles [sw26162]
  • scalability and generalization of the spectral embedding (i.e., out-of-sample-extension). In this paper ... points into the eigenspace of their associated graph Laplacian matrix and subsequently clusters them ... stochastic optimization. Stochastic optimization allows it to scale to large datasets, while the constraints, which ... learned by SpectralNet naturally generalizes the spectral embedding to unseen data points. To further improve...
  • CogDL

  • Referenced in 2 articles [sw37740]
  • aims to learn low-dimensional node embeddings for graphs. It is used in several real ... such as social network analysis and large-scale recommender systems. In this paper, we introduce ... graph domain, including node classification, link prediction, graph classification, and other graph tasks. For each ... divided into two major parts, graph embedding methods and graph neural networks. Most...
  • ProNE

  • Referenced in 1 article [sw37757]
  • graph and network mining. However, (pre-)training embeddings for very large-scale networks is computationally...
  • OpenGraphGym

  • Referenced in 1 article [sw38102]
  • embedding methods for classic graph problems; 2) advanced parallel strategies for extreme-scale graph computations...
  • NodeSketch

  • Referenced in 1 article [sw32346]
  • embeddings via stochastic optimization, or factorize a high-order proximity/adjacency matrix of the graph ... which hinders their applications on large-scale graphs. Moreover, the cosine similarity preserved by these ... techniques shows suboptimal efficiency in downstream graph analysis tasks, compared to Hamming similarity, for example ... propose NodeSketch, a highly-efficient graph embedding technique preserving high-order node proximity via recursive...
  • PlantGL

  • Referenced in 2 articles [sw23586]
  • library for 3D plant modelling at different scales. In this paper, we present PlantGL ... virtual plants. This C++ geometric library is embedded in the Python language which makes ... plant populations. Based on a scene graph augmented with primitives dedicated to plant representation, several ... analyse or manipulate geometric models at different scales ranging from tissues to plant communities...
  • SINE

  • Referenced in 1 article [sw32344]
  • scale networks often have incomplete/missing node content or linkages, yet existing attributed network embedding algorithms ... Incomplete Network Embedding (SINE) algorithm for learning node representations from incomplete graphs. SINE formulates ... attribute relationships. Different from existing attributed network embedding algorithms, SINE provides greater flexibility to make ... learn node representations, allowing SINE to scale up to large-scale networks with high learning...
  • NScaleSpark

  • Referenced in 1 article [sw41762]
  • NScaleSpark, a framework for executing large-scale distributed graph analysis tasks on the Apache Spark ... over large graph datasets. There is much recent work on vertex-centric graph programming frameworks ... paradigm is not suitable for many complex graph analysis tasks that typically require processing ... memory graph data structure enables efficient graph computations over large-scale graphs. Our experimental results...
  • dypro

  • Referenced in 1 article [sw28851]
  • Neural program embedding has shown potential in aiding the analysis of large-scale, complicated software ... against two prominent static models: Gated Graph Neural Network and TreeLSTM. We find that dypro...
  • AMD

  • Referenced in 60 articles [sw00039]
  • Algorithm 837: AMD is a set of routines...
  • ANSYS

  • Referenced in 704 articles [sw00044]
  • ANSYS offers a comprehensive software suite that spans...
  • ATLAS

  • Referenced in 199 articles [sw00056]
  • This paper describes the Automatically Tuned Linear Algebra...
  • BoomerAMG

  • Referenced in 196 articles [sw00086]
  • BoomerAMG: A parallel algebraic multigrid solver and preconditioner...