scholarly journals Entity Local Structure Graph Matching for Mislabeling Correction

2016 ◽  
Author(s):  
Nihel Kooli ◽  
Abdel Belaid ◽  
Aurelie Joseph ◽  
Vincent Poulain D'Andecy
Keyword(s):  
Local Structure ◽  
Graph Matching ◽  
Structure Graph

Modeling Transitivity in Local Structure Graph Models

Sankhya A ◽  
2021 ◽  
Author(s):  
Emily Casleton ◽  
Daniel J. Nordman ◽  
Mark S. Kaiser
Keyword(s):  
Local Structure ◽  
Graph Models ◽  
Structure Graph

scholarly journals Local structure graph models with higher‐order dependence

2020 ◽  
Author(s):  
Emily M. Casleton ◽  
Daniel J. Nordman ◽  
Mark S. Kaiser
Keyword(s):  
Local Structure ◽  
Higher Order ◽  
Graph Models ◽  
Order Dependence ◽  
Structure Graph

scholarly journals A Local Structure Graph Model: Modeling Formation of Network Edges as a Function of Other Edges

2019 ◽  
Vol 27 (4) ◽  
pp. 397-414 ◽  
Author(s):  
Olga V. Chyzh ◽  
Mark S. Kaiser
Keyword(s):  
Local Structure ◽  
Graph Model ◽  
Theoretical Framework ◽  
Monte Carlo Experiment ◽  
The Us ◽  
Us Senate ◽  
Cascade Effects ◽  
Network Approaches ◽  
Reaction Processes ◽  
Structure Graph

Localized network processes are central to the study of political science, whether in the formation of political coalitions and voting blocs, balancing and bandwagoning, policy learning, imitation, diffusion, tipping-point dynamics, or cascade effects. These types of processes are not easily modeled using traditional network approaches, which focus on global rather than local structures within networks. We show that localized network processes, in which network edges form in response to the formation of other edges, are best modeled by shifting from the traditional theoretical framework of nodes-as-actors to what we term a nodes-as-actions framework, which allows for zeroing in on relationships among network connections. We show that the proposed theoretical framework is statistically compatible with a local structure graph model (LSGM). We demonstrate the properties of LSGMs using a Monte Carlo experiment and explore action–reaction processes in two empirical applications: formation of alliances among countries and legislative cosponsorships in the US Senate.

scholarly journals A Deep Local and Global Scene-Graph Matching for Image-Text Retrieval

2021 ◽  
Author(s):  
Manh-Duy Nguyen ◽  
Binh T. Nguyen ◽  
Cathal Gurrin
Keyword(s):  
Graph Matching ◽  
Text Retrieval ◽  
General Information ◽  
Suitable Method ◽  
Baseline Method ◽  
Scene Graph ◽  
Visual Objects ◽  
Convolution Model ◽  
Graph Presentation ◽  
Structure Graph

Conventional approaches to image-text retrieval mainly focus on indexing visual objects appearing in pictures but ignore the interactions between these objects. Such objects occurrences and interactions are equivalently useful and important in this field as they are usually mentioned in the text. Scene graph presentation is a suitable method for the image-text matching challenge and obtained good results due to its ability to capture the inter-relationship information. Both images and text are represented in scene graph levels and formulate the retrieval challenge as a scene graph matching challenge. In this paper, we introduce the Local and Global Scene Graph Matching (LGSGM) model that enhances the state-of-the-art method by integrating an extra graph convolution network to capture the general information of a graph. Specifically, for a pair of scene graphs of an image and its caption, two separate models are used to learn the features of each graph’s nodes and edges. Then a Siamese-structure graph convolution model is employed to embed graphs into vector forms. We finally combine the graph-level and the vector-level to calculate the similarity of this image-text pair. The empirical experiments show that our enhancement with the combination of levels can improve the performance of the baseline method by increasing the recall by more than 10% on the Flickr30k dataset. Our implementation code can be found at https://github.com/m2man/LGSGM.

X-ray microprobe for materials science

1994 ◽  
Vol 52 ◽  
pp. 56-57
Author(s):  
G.E. Ice
Keyword(s):  
Crystallographic Orientation ◽  
Local Structure ◽  
Materials Science ◽  
Chemical Information ◽  
X Ray Diffraction ◽  
Ray Optics ◽  
X Ray ◽  
Novel Materials ◽  
New Information ◽  
Elemental Sensitivity

The increasing availability of synchrotron x-ray sources has stimulated the development of advanced hard x-ray (E≥5 keV) microprobes. With new x-ray optics these microprobes can achieve micron and submicron spatial resolutions. The inherent elemental and crystallographic sensitivity of an x-ray microprobe and its inherently nondestructive and penetrating nature will have important applications to materials science. For example, x-ray fluorescent microanalysis of materials can reveal elemental distributions with greater sensitivity than alternative nondestructive probes. In materials, segregation and nonuniform distributions are the rule rather than the exception. Common interfaces to whichsegregation occurs are surfaces, grain and precipitate boundaries, dislocations, and surfaces formed by defects such as vacancy and interstitial configurations. In addition to chemical information, an x-ray diffraction microprobe can reveal the local structure of a material by detecting its phase, crystallographic orientation and strain.Demonstration experiments have already exploited the penetrating nature of an x-ray microprobe and its inherent elemental sensitivity to provide new information about elemental distributions in novel materials.

DETERMINATION OF THE LOCAL STRUCTURE OF METALLIC GLASSES BY EXAFS

1982 ◽  
Vol 43 (C9) ◽  
pp. C9-43-C9-46 ◽  
Author(s):  
A. Sadoc ◽  
A. M. Flank ◽  
D. Raoux ◽  
P. Lagarde
Keyword(s):  
Metallic Glasses ◽  
Local Structure
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