• FSCNMF

  • Referenced in 1 article [sw32347]
  • network. Network embedding learns a compact low-dimensional vector representation for each node ... current embedding algorithms. However, some content is associated with each node, in most ... each node in the current state-of-the-art network embedding methods. In this paper ... network structure and the content of the nodes while learning a lower dimensional representation...
  • Rigel

  • Referenced in 2 articles [sw40556]
  • approach that accurately approximates node distances in constant time by embedding graphs into coordinate spaces ... show that a hyperbolic embedding produces relatively low distortion error, and propose Rigel, a hyperbolic ... results for graphs up to 43 million nodes. Finally, we show that Rigel’s functionality...
  • HeteSpaceyWalk

  • Referenced in 1 article [sw39745]
  • current way of random-walk based HIN embedding methods have paid few attention ... effectively attain the expected stationary distribution among nodes. Then we propose a generalized scalable framework ... random walk to learn embeddings for multiple types of nodes in an HIN guided ... existing state-of-the-art network embedding algorithms...
  • AutoNE

  • Referenced in 2 articles [sw38085]
  • Massive Network Embedding. Network embedding (NE) aims to embed the nodes of a network into...
  • Scikit-network

  • Referenced in 1 article [sw37086]
  • algorithms for ranking, clustering, classifying, embedding and visualizing the nodes of a graph. High performance...
  • SWATT

  • Referenced in 5 articles [sw09061]
  • SWATT) to verify the memory contents of embedded devices and establish the absence of malicious ... program memory even while the sensor node is running...
  • DGL-KE

  • Referenced in 3 articles [sw34088]
  • Training Knowledge Graph Embeddings at Scale. Knowledge graphs (KGs) are data structures that store information ... about different entities (nodes) and their relations (edges). A common approach of using ... learning tasks is to compute knowledge graph embeddings. DGL-KE is a high performance, easy...
  • PyMDE

  • Referenced in 1 article [sw40504]
  • computing vector embeddings of items, such as images, biological cells, nodes in a network...
  • BioKEEN

  • Referenced in 3 articles [sw34085]
  • evaluating biological knowledge graph embeddings. Knowledge graph embeddings (KGEs) have received significant attention in other ... 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...
  • iMPTCE-Hnetwork

  • Referenced in 1 article [sw39601]
  • embedding features derived from a heterogeneous network, which defined chemicals and enzymes as nodes...
  • DINS

  • Referenced in 8 articles [sw10145]
  • variation of local search that is embedded in an exact MIP solver, namely a branch ... solution to define search neighbourhoods at different nodes of the search tree generated...
  • Chromium

  • Referenced in 5 articles [sw14150]
  • customize the stream transformations performed by nodes in a cluster. Because our stream processing mechanism ... either implemented on top of or embedded in Chromium. In this paper, we give examples...
  • ScalaBLAST

  • Referenced in 7 articles [sw08812]
  • matching problem focused on unlocking protein information embedded in the genetic code, making it possible ... data is growing more rapidly than per-node core memory. Despite years of research...
  • Sub2vec

  • Referenced in 1 article [sw41562]
  • network embeddings in order to exploit machine learning algorithms for mining tasks like node classification ... work focuses on distributed representations of nodes that are inherently ill-suited to tasks such ... dependent on subgraphs. Here, we formulate subgraph embedding problem based on two intuitive properties...
  • graph2vec

  • Referenced in 10 articles [sw32340]
  • distributed representations of graph substructures such as nodes and subgraphs. However, many graph analytics tasks ... this work, we propose a neural embedding framework named graph2vec to learn data-driven distributed...
  • NetSMF

  • Referenced in 1 article [sw37752]
  • embeddings for a large-scale academic collaboration network with tens of millions of nodes, while...
  • Argo Lite

  • Referenced in 1 article [sw38750]
  • embedded web widgets. Users can explore graphs incrementally by adding more related nodes, such...
  • ProNE

  • Referenced in 1 article [sw37757]
  • faster than efficient network embedding benchmarks with 20 threads, including LINE, DeepWalk, node2vec, GraRep ... network of hundreds of millions of nodes while it takes LINE weeks and DeepWalk months ... achieve this, ProNE first initializes network embeddings efficiently by formulating the task as sparse matrix...
  • gl2vec

  • Referenced in 1 article [sw41561]
  • temporal changes. Furthermore, most work focuses on node representations that do poorly on tasks like ... this paper, we propose a novel network embedding methodology, gl2vec, for network classification in both...
  • PHAST

  • Referenced in 2 articles [sw11517]
  • with the geochemical model PHREEQC, which is embedded in PHAST. Major enhancements in PHAST Version ... independent of a specific model grid (without node-by-node input). At run time, aquifer...