• GraRep

  • Referenced in 22 articles [sw32342]
  • GraRep: Learning Graph Representations with Global Structural Information. In this paper, we present GraRep ... novel model for learning vertex representations of weighted graphs. This model learns low dimensional vectors ... global structural information of the graph into the learning process. We also formally analyze ... citation network and show that our learned global representations can be effectively used as features...
  • graph2vec

  • Referenced in 10 articles [sw32340]
  • Graphs. Recent works on representation learning for graph structured data predominantly focus on learning distributed ... approaches are naturally unequipped to learn such representations, graph kernels remain as the most effective ... named graph2vec to learn data-driven distributed representations of arbitrary sized graphs. graph2vec’s embeddings ... task such as graph classification, clustering and even seeding supervised representation learning approaches. Our experiments...
  • DeepWalk

  • Referenced in 63 articles [sw39604]
  • vertices in a network. These latent representations encode social relations in a continuous vector space ... unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local...
  • SVMlight

  • Referenced in 264 articles [sw04076]
  • functions [Joachims, 2002c]. The goal is to learn a function from preference examples, so that ... Nearest Neighbor is the Spectral Graph Transducer. SVMlight can also train SVMs with cost models ... leads to a very compact and efficient representation...
  • Pykg2vec

  • Referenced in 5 articles [sw30609]
  • library for learning the representations of the entities and relations in knowledge graphs. Pykg2vec ... platform to accelerate research in knowledge graph representation learning. Pykg2vec is built...
  • dyngraph2vec

  • Referenced in 3 articles [sw40462]
  • Network Dynamics using Dynamic Graph Representation Learning. Learning graph representations is a fundamental task aimed ... properties of graphs in vector space. The most recent methods learn such representations for static ... embedding approach which learns the structure of evolution in dynamic graphs and can predict unseen...
  • subgraph2vec

  • Referenced in 4 articles [sw36496]
  • subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs. In this paper ... novel approach for learning latent representations of rooted subgraphs from large graphs inspired ... recent advancements in Deep Learning and Graph Kernels. These latent representations encode semantic substructure dependencies ... from neighbourhoods of nodes to learn their latent representations in an unsupervised fashion. We demonstrate...
  • InfoGraph

  • Referenced in 2 articles [sw37754]
  • InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. This paper ... studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph ... representation learning, in this paper we proposed a novel method called InfoGraph for learning graph ... maximizes the mutual information between unsupervised graph representations learned by InfoGraph and the representations learned...
  • CogDL

  • Referenced in 2 articles [sw37740]
  • Extensive Toolkit for Deep Learning on Graphs. Graph representation learning aims to learn low-dimensional ... graph embedding methods learn node-level or graph-level representations in an unsupervised...
  • AmpliGraph

  • Referenced in 5 articles [sw30610]
  • AmpliGraph: a Library for Representation Learning on Knowledge Graphs. Open source library based on TensorFlow...
  • GraphSAGE

  • Referenced in 4 articles [sw33908]
  • framework for inductive representation learning on large graphs. GraphSAGE is used to generate low-dimensional...
  • struc2vec

  • Referenced in 13 articles [sw36495]
  • novel and flexible framework for learning latent representations for the structural identity of nodes. struc2vec ... different scales, and constructs a multilayer graph to encode structural similarities and generate structural context ... state-of-the-art techniques for learning node representations fail in capturing stronger notions...
  • persona2vec

  • Referenced in 1 article [sw33469]
  • Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs. Graph embedding techniques, which learn ... propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based ... Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state ... propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based...
  • VeRNAl

  • Referenced in 1 article [sw39988]
  • motif finding problem as a graph representation learning and clustering task. This framing takes advantage...
  • GripNet

  • Referenced in 1 article [sw39161]
  • Supergraph for Heterogeneous Graphs. Heterogeneous graph representation learning aims to learn low-dimensional vector representations ... this paper proposes a flexible and efficient Graph information propagation Network (GripNet) framework. Specifically ... propagation path between two supervertices. GripNet learns new representations for the supervertex of interest ... multiple layers. We construct multiple large-scale graphs and evaluate GripNet against competing methods...
  • NodeSketch

  • Referenced in 1 article [sw32346]
  • have become a key paradigm to learn graph representations and facilitate downstream graph analysis tasks ... number of node pairs from a graph to learn node embeddings via stochastic optimization...
  • QuteSAT

  • Referenced in 3 articles [sw11382]
  • implicit implication graph representation for efficient learning, and (3) careful engineering on the most advanced...
  • NeuralKG

  • Referenced in 1 article [sw41382]
  • Open Source Library for Diverse Representation Learning of Knowledge Graphs. NeuralKG is an open-source ... Python-based library for diverse representation learning of knowledge graphs. It implements three different series ... Knowledge Graph Embedding (KGE) methods, including conventional KGEs, GNN-based KGEs, and Rule-based KGEs ... organize an open and shared KG representation learning community. The source code is all publicly...
  • TransT

  • Referenced in 2 articles [sw34445]
  • Knowledge Graph Completion. Knowledge graph completion with representation learning predicts new entity-relation triples from ... neglect semantic information contained in most knowledge graphs and the prior knowledge indicated ... prior distributions, our approach generates multiple embedding representations of each entity in different contexts...
  • ProGraML

  • Referenced in 1 article [sw32379]
  • ProGraML: Graph-based Deep Learning for Program Optimization and Analysis. The increasing complexity of computing ... ever more accurate and aggressive optimizations. Machine learning offers significant benefits for constructing optimization heuristics ... Program Graphs for Machine Learning - a novel graph-based program representation using a low level ... learning models capable of performing complex downstream tasks over these graphs. The ProGraML representation...