• BioGRID

  • Referenced in 48 articles [sw17422]
  • variety of file formats. User-customized graphs with embedded protein, gene and interaction attributes...
  • Groups & Graphs

  • Referenced in 10 articles [sw15344]
  • software package for graphs, digraphs, graph embeddings, projective configurations, polyhedra, convex hulls, combinatorial designs, automorphism...
  • AGREE

  • Referenced in 10 articles [sw23738]
  • AGREE – algebraic graph rewriting with controlled embedding. The several algebraic approaches to graph transformation proposed ... connections with the context graph where it is embedded. But there are applications in which ... desirable to specify different embeddings. For example when cloning an item, there ... conservative extension of classical algebraic approaches to graph transformation, for the case of monic matches...
  • Pykg2vec

  • Referenced in 5 articles [sw30609]
  • Pykg2vec: A Python Library for Knowledge Graph Embedding. Pykg2vec is an open-source Python library ... entities and relations in knowledge graphs. Pykg2vec’s flexible and modular software architecture currently implements ... state-of-the-art knowledge graph embedding algorithms, and is designed to easily incorporate ... educational platform to accelerate research in knowledge graph representation learning. Pykg2vec is built...
  • t-SNE

  • Referenced in 176 articles [sw22300]
  • technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much ... 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...
  • DynGEM

  • Referenced in 4 articles [sw40461]
  • DynGEM: Deep Embedding Method for Dynamic Graphs. Embedding large graphs in low dimensional spaces ... methods focus on computing the embedding for static graphs. However, many graphs in practical applications ... Naively applying existing embedding algorithms to each snapshot of dynamic graphs independently usually leads ... recent advances in deep autoencoders for graph embeddings, to address this problem. The major advantages...
  • 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 ... train and evaluate embeddings on several large social network graphs as well as the full...
  • AnnexML

  • Referenced in 5 articles [sw30153]
  • this paper, we present a novel graph embedding method called ”AnnexML”. At the training step ... attempts to reproduce the graph structure in the embedding space. The prediction is efficiently performed ... learned k-nearest neighbor graph in the embedding space. We conducted evaluations on several large...
  • CHOMPACK

  • Referenced in 14 articles [sw04593]
  • Covariance selection for non-chordal graphs via chordal embedding by J. Dahl, L. Vandenberghe...
  • Orb

  • Referenced in 6 articles [sw34482]
  • start with a projection of a graph embedded in the 3-sphere, and produce ... skeleton and the remainder of the graph drilled out. It enables computation of hyperbolic structures...
  • graph2vec

  • Referenced in 10 articles [sw32340]
  • effective way of obtaining them. However, these graph kernels use handcrafted features (e.g., shortest paths ... this work, we propose a neural embedding framework named graph2vec to learn data-driven ... distributed representations of arbitrary sized graphs. graph2vec’s embeddings are learnt in an unsupervised manner ... used for any downstream task such as graph classification, clustering and even seeding supervised representation...
  • TransG

  • Referenced in 3 articles [sw34443]
  • Generative Mixture Model for Knowledge Graph Embedding. Recently, knowledge graph embedding, which projects symbolic entities ... mixture of relation component vectors for embedding a fact triple. To the best ... first generative model for knowledge graph embedding, which is able to deal with multiple relation...
  • 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 ... package for learning large-scale knowledge graph embeddings. The package is implemented...
  • AmpliGraph

  • Referenced in 5 articles [sw30610]
  • missing statements. Generate stand-alone knowledge graph embeddings. Develop and evaluate a new relational model...
  • BioKEEN

  • Referenced in 3 articles [sw34085]
  • learning and evaluating biological knowledge graph embeddings. Knowledge graph embeddings (KGEs) have received significant attention ... predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem ... learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate...
  • Graphs

  • Referenced in 109 articles [sw12277]
  • first presented with a network (graph). A so-called preprocessing algorithm may compute certain information ... networking and distributed computing and for metric embeddings in geometry as well. In this survey ... query time. We survey methods for general graphs as well as specialized methods for restricted...
  • RotatE

  • Referenced in 3 articles [sw37755]
  • RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. We study the problem ... representations of entities and relations in knowledge graphs for predicting missing links. The success ... present a new approach for knowledge graph embedding called RotatE, which is able to model ... model. Experimental results on multiple benchmark knowledge graphs show that the proposed RotatE model...
  • PyKEEN

  • Referenced in 3 articles [sw34084]
  • Evaluating Knowledge Graph Emebddings. Recently, knowledge graph embeddings (KGEs) received significant attention, and several software ... enables users to compose knowledge graph embedding models (KGEMs) based on a wide range...
  • GraphVite

  • Referenced in 2 articles [sw34087]
  • GraphVite - graph embedding at high speed and large scale. GraphVite is a general graph embedding ... applications: node embedding, knowledge graph embedding and graph & high-dimensional data visualization. Besides, it also...
  • LibKGE

  • Referenced in 2 articles [sw39398]
  • LibKGE - A knowledge graph embedding library for reproducible research. LibKGE ( https://github.com/uma-pi1/kge ... hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction. The key goals ... LibKGE provides implementations of common knowledge graph embedding models and training methods, and new ones...