• metapath2vec

  • Referenced in 10 articles [sw37749]
  • nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop ... construct the heterogeneous neighborhood of a node and then leverages ... heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous ... embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity...
  • KLEIN

  • Referenced in 20 articles [sw09457]
  • efficiency in embedded systems like RFID tags and sensor nodes. Among those primitives, lightweight block...
  • Cellerator

  • Referenced in 8 articles [sw09926]
  • represented as graphs with STNs embedded in each node. Interactions include mass-action, enzymatic, allosteric...
  • GLINTS

  • Referenced in 3 articles [sw40628]
  • narrowing computations, (iii) automatic checking of nodeembedding’ and ’closedness’ modulo axioms, and (iv) querying...
  • NodeSketch

  • Referenced in 1 article [sw32346]
  • pairs from a graph to learn node embeddings via stochastic optimization, or factorize a high ... data-independent hashing/sketching technique, NodeSketch generates node embeddings in Hamming space. For an input graph ... graph to output low-order node embeddings, and then recursively generates k-order node embeddings ... adjacency matrix and (k-1)-order node embeddings. Our extensive evaluation compares NodeSketch against...
  • PyTorch-BigGraph

  • Referenced in 4 articles [sw34086]
  • scale Graph Embedding System. Graph embedding methods produce unsupervised node features from graphs that ... nodes and trillions of edges, which exceeds the capability of existing embedding systems. We present ... relation embedding systems that allow it to scale to graphs with billions of nodes ... multiple machines. We train and evaluate embeddings on several large social network graphs as well...
  • CogDL

  • Referenced in 2 articles [sw37740]
  • learning aims to learn low-dimensional node embeddings for graphs. It is used in several ... important tasks in the graph domain, including node classification, link prediction, graph classification, and other ... divided into two major parts, graph embedding methods and graph neural ... networks. Most of the graph embedding methods learn node-level or graph-level representations...
  • GraphVite

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

  • Referenced in 1 article [sw38087]
  • where repeated graph convolutions eventually make node embeddings indistinguishable. We take a closer look ... graph convolution operator, which prevents all node embeddings from becoming too similar. What is more...
  • KGAT

  • Referenced in 2 articles [sw32568]
  • fashion. It recursively propagates the embeddings from a node’s neighbors (which can be users ... items, or attributes) to refine the node’s embedding, and employs an attention mechanism...
  • FedGL

  • Referenced in 1 article [sw41836]
  • upload the prediction results and node embeddings to the server for discovering the global pseudo...
  • SINE

  • Referenced in 1 article [sw32344]
  • exhibit high correlation, incorporating node attribute proximity into network embedding is beneficial for learning good ... often have incomplete/missing node content or linkages, yet existing attributed network embedding algorithms all operate ... Scalable Incomplete Network Embedding (SINE) algorithm for learning node representations from incomplete graphs. SINE formulates ... node-context and node-attribute relationships. Different from existing attributed network embedding algorithms, SINE provides...
  • Flask

  • Referenced in 5 articles [sw09691]
  • have developed Flask, a domain specific language embedded in Haskell that brings the power ... staging mechanism that cleanly separates node-level code from the meta-language ... used to generate node-level code fragments; syntactic support for embedding standard sensor network code ... well as a compiler that generates node-level code to execute a network-wide query...
  • SONET

  • Referenced in 22 articles [sw10644]
  • cable cut (link) or an equipment failure (node). The Synchronous Optical NETwork (SONET) makes these ... reads in data about the network, its embedded capacity, the available equipment, the customer demands...
  • persona2vec

  • Referenced in 1 article [sw33469]
  • tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that ... persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their ... tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that ... persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their...
  • DynGEM

  • Referenced in 4 articles [sw40461]
  • DynGEM: Deep Embedding Method for Dynamic Graphs. Embedding large graphs in low dimensional spaces ... link prediction and node classification. Existing methods focus on computing the embedding for static graphs...
  • HIN2Vec

  • Referenced in 6 articles [sw37750]
  • rich semantics embedded in HINs by exploiting different types of relationships among nodes. Given...
  • Karate Club

  • Referenced in 2 articles [sw32339]
  • make community detection, node and whole graph embedding available to a wide audience of machine...
  • HyPy

  • Referenced in 2 articles [sw40557]
  • method by embedding a graph with 1.8 billion edges and 65 million nodes...
  • 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...