• RotatE

  • Referenced in 3 articles [sw37755]
  • problem of learning representations of entities and relations in knowledge graphs for predicting missing links...
  • GraphSAINT

  • Referenced in 1 article [sw37758]
  • GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large ... training. We propose GraphSAINT, a graph sampling based inductive learning method that improves training efficiency...
  • SINE

  • Referenced in 1 article [sw32344]
  • Embedding (SINE) algorithm for learning node representations from incomplete graphs. SINE formulates a probabilistic learning ... mitigate negative effects of missing information on representation learning. A stochastic gradient descent based online...
  • Devign

  • Referenced in 1 article [sw40145]
  • Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. Vulnerability identification ... from various code representation graphs and the recent advance on graph neural networks, we propose ... general graph neural network based model for graph-level classification through learning on a rich ... useful features in the learned rich node representations for graph-level classification. The model...
  • FedGraphNN

  • Referenced in 1 article [sw41837]
  • System and Benchmark for Graph Neural Networks. Graph Neural Network (GNN) research is rapidly growing ... capacity of GNNs in learning distributed representations from graph-structured data. However, centralizing a massive ... concerns, regulation restrictions, and commercial competitions. Federated learning (FL), a trending distributed learning paradigm, provides...
  • RNAglib

  • Referenced in 1 article [sw39986]
  • efficiently exploited by graph theoretical approaches and recent progresses in machine learning techniques. RNAglib ... library that eases the use of this representation, by providing clean data, methods to load ... learning pipelines and graph-based deep learning models suited for this representation. RNAglib also offers ... utilities to model RNA with 2.5D graphs, such as drawing tools, comparison functions...
  • AGL-Score

  • Referenced in 2 articles [sw41015]
  • geometry relationship is provided. Novel algebraic graph learning score (AGL-Score) models are proposed ... biological information into intrinsically low-dimensional representations. The proposed AGL-Score models employ multiscale weighted ... biomolecular interactions in terms of graph invariants derived from graph Laplacian, its pseudo-inverse ... machine learning algorithm to predict biomolecular macroscopic properties from the low-dimensional graph representation...
  • OpenKE

  • Referenced in 5 articles [sw30611]
  • various fundamental models to embed knowledge graphs into a continuous low-dimensional space. OpenKE prioritizes ... model validation and large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity and extensibility ... embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE are also available...
  • KnowledgeSeeker

  • Referenced in 3 articles [sw25440]
  • knowledge representation model Ontology Graph. Second, it contains an ontology learning process that -- based...
  • MathCheck

  • Referenced in 13 articles [sw13642]
  • universal conjectures on any mathematical topic (e.g., graph and number theory, algebra, geometry, etc.) supported ... space of the SAT solver, by providing learned clauses that encode theory-specific lemmas ... pure Boolean representation.{par}In this paper, we leverage the graph-theoretic capabilities...
  • kLog

  • Referenced in 6 articles [sw10403]
  • builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, logic programming ... call graphicalization: the relational representation is first transformed into a graph -- in particular, a grounded ... entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. kLog supports mixed ... tasks that has made statistical relational learning so popular, including classification, regression, multitask learning...
  • BioKEEN

  • Referenced in 3 articles [sw34085]
  • library for learning and evaluating biological knowledge graph embeddings. Knowledge graph embeddings (KGEs) have received ... predict links and create dense representations for graphs’ nodes and edges. However, the software ecosystem ... users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs...
  • CLEVR Parser

  • Referenced in 1 article [sw35072]
  • Machine Learning (ML) and Natural Language Processing (NLP) domains. We present a graph parser library ... structural graph representations for dual modalities. Structural order-invariant representations enable geometric learning ... that can be tailored to suit specific learning setups. We also provide ... functionality for seamless integration with popular deep graph neural network (GNN) libraries. Additionally, we discuss...
  • MuACOsm

  • Referenced in 2 articles [sw17580]
  • learning Finite-State Machines (FSM) based on Ant Colony Optimization (ACO) and a graph representation...
  • ProNE

  • Referenced in 1 article [sw37757]
  • Network Representation Learning. Recent advances in network embedding has revolutionized the field of graph...
  • HistoCartography

  • Referenced in 1 article [sw39628]
  • learn the tissue structure-to-function relationship. Entity-graphs offer flexible and scalable representations ... explainability. However, entity-graph analysis requires prerequisites for image-to-graph translation and knowledge ... state-of-the-art machine learning algorithms applied to graph-structured data, which can potentially...
  • MolGAN

  • Referenced in 3 articles [sw36059]
  • directly on graph-structured data. We combine our approach with a reinforcement learning objective ... SMILES) representations of molecules and to a likelihood-based method that directly generates graphs, albeit...
  • FeaStNet

  • Referenced in 1 article [sw40969]
  • relying on predefined static coordinates over the graph as in previous work. We obtain excellent ... shows that our approach can learn effective shape representations from raw input coordinates, without relying...
  • Manifold Regularization

  • Referenced in 1 article [sw24840]
  • general-purpose learner. Some transductive graph learning algorithms and standard methods including support vector machines ... reproducing kernel Hilbert spaces to prove new Representer theorems that provide theoretical basis ... result (in contrast to purely graph-based approaches) we obtain a natural out-of-sample ... brief discussion of unsupervised and fully supervised learning within our general framework...
  • MIxBN

  • Referenced in 1 article [sw39155]
  • launching learning algorithms on one of two algorithms for enumerating graph structures - Hill-Climbing ... algorithm. Since the need for mixed data representation comes from practical necessity, the advantages...