scholarly journals A Technical Translation of Melentiev’s Graph Representation Method with Commentary

2018 ◽  
Author(s):  
Asya Volkova
Keyword(s):  
Graph Representation ◽  
Technical Translation ◽  
Representation Method

scholarly journals Knowledge Graph Representation via Similarity-Based Embedding

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Zhen Tan ◽  
Xiang Zhao ◽  
Yang Fang ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Large Scale ◽  
Low Complexity ◽  
Graph Representation ◽  
Knowledge Graph ◽  
Continuous Space ◽  
Memory Space ◽  
Representation Method ◽  
Relation Prediction ◽  
Low Dimensional ◽  
Almost All

Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet it is still far away from completeness. Knowledge graph embedding, as a representation method, constructs a low-dimensional and continuous space to describe the latent semantic information and predict the missing facts. Among various solutions, almost all embedding models have high time and memory-space complexities and, hence, are difficult to apply to large-scale knowledge graphs. Some other embedding models, such as TransE and DistMult, although with lower complexity, ignore inherent features and only use correlations between different entities to represent the features of each entity. To overcome these shortcomings, we present a novel low-complexity embedding model, namely, SimE-ER, to calculate the similarity of entities in independent and associated spaces. In SimE-ER, each entity (relation) is described as two parts. The entity (relation) features in independent space are represented by the features entity (relation) intrinsically owns and, in associated space, the entity (relation) features are expressed by the entity (relation) features they connect. And the similarity between the embeddings of the same entities in different representation spaces is high. In experiments, we evaluate our model with two typical tasks: entity prediction and relation prediction. Compared with the state-of-the-art models, our experimental results demonstrate that SimE-ER outperforms existing competitors and has low time and memory-space complexities.

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

Calculations of Polarizabilities and Their Gradients for Electron Energy-Loss Spectroscopy

2008 ◽  
Vol 73 (11) ◽  
pp. 1509-1524 ◽  
Author(s):  
Ivana Paidarová ◽  
Roman Čurík ◽  
Stephan P. A. Sauer
Keyword(s):  
Energy Loss ◽  
Electron Energy ◽  
Theoretical Models ◽  
Basis Set ◽  
Polarizability Tensor ◽  
Dipole Polarizability ◽  
Perfect Agreement ◽  
Representation Method ◽  
Response Method ◽  
Electron Energy Loss

We illustrate for a set of small hydrocarbons, CH4, C2H4, C3H6 and C3H8, the important role of the electric dipole polarizability tensor and its geometric derivatives in theoretical models of electron energy-loss spectra (EELS). The coupled cluster linear response method together with Sadlej's polarized valence triple zeta basis set of atomic orbitals were used to calculate the polarizabilities and polarizability gradients. Incorporation of these ab initio data into the discrete momentum representation method (DMR) leads to perfect agreement between theory and collision experiments.

scholarly journals Graph Representation Ensemble Learning

2020 ◽  
Author(s):  
Palash Goyal ◽  
Sachin Raja ◽  
Di Huang ◽  
Sujit Rokka Chhetri ◽  
Arquimedes Canedo ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Graph Representation

scholarly journals A Survey of Information Cascade Analysis

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Fan Zhou ◽  
Xovee Xu ◽  
Goce Trajcevski ◽  
Kunpeng Zhang
Keyword(s):  
Information Diffusion ◽  
Research Work ◽  
Viral Marketing ◽  
Graph Representation ◽  
Digital Information ◽  
Information Cascades ◽  
Challenges And Opportunities ◽  
Scientific Papers ◽  
Popularity Prediction ◽  
Types Of Information

The deluge of digital information in our daily life—from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising—offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes , through graph representation , to deep learning-based approaches . Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.

Sign in / Sign up

Export Citation Format

Share Document