scholarly journals Hierarchical Graph Representation for Symbol Spotting in Graphical Document Images

2012 ◽  
pp. 529-538 ◽  
Author(s):  
Klaus Broelemann ◽  
Anjan Dutta ◽  
Xiaoyi Jiang ◽  
Josep Lladós
Keyword(s):  
Graph Representation ◽  
Document Images ◽  
Hierarchical Graph

Self-supervised Hierarchical Graph Neural Network for Graph Representation

2020 ◽  
Author(s):  
Sambaran Bandyopadhyay ◽  
Manasvi Aggarwal ◽  
M. Narasimha Murty
Keyword(s):  
Neural Network ◽  
Graph Representation ◽  
Hierarchical Graph

scholarly journals ACE: Ant Colony Based Multi-Level Network Embedding for Hierarchical Graph Representation Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 73970-73982 ◽  
Author(s):  
Jianming Lv ◽  
Jiajie Zhong ◽  
Jintao Liang ◽  
Zhenguo Yang
Keyword(s):  
Representation Learning ◽  
Ant Colony ◽  
Graph Representation ◽  
Network Embedding ◽  
Hierarchical Graph ◽  
Multi Level

scholarly journals CommPOOL: An interpretable graph pooling framework for hierarchical graph representation learning

2021 ◽  
Author(s):  
Haoteng Tang ◽  
Guixiang Ma ◽  
Lifang He ◽  
Heng Huang ◽  
Liang Zhan
Keyword(s):  
Representation Learning ◽  
Graph Representation ◽  
Hierarchical Graph

Hierarchical Graph Representation Learning with Local Capsule Pooling

2021 ◽  
Author(s):  
Zidong Su ◽  
Zehui Hu ◽  
Yangding Li
Keyword(s):  
Representation Learning ◽  
Graph Representation ◽  
Hierarchical Graph

scholarly journals Hierarchical Graph Representation of Pharmacophore Models

2020 ◽  
Vol 7 ◽  
Author(s):  
Garon Arthur ◽  
Wieder Oliver ◽  
Bareis Klaus ◽  
Seidel Thomas ◽  
Ibis Gökhan ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Large Scale ◽  
Md Simulations ◽  
Graph Representation ◽  
Feature Interaction ◽  
Full Potential ◽  
Interaction Patterns ◽  
Ligand Interaction ◽  
Hierarchical Graph ◽  
Pharmacophore Models

For the investigation of protein-ligand interaction patterns, the current accessibility of a wide variety of sampling methods allows quick access to large-scale data. The main example is the intensive use of molecular dynamics simulations applied to crystallographic structures which provide dynamic information on the binding interactions in protein-ligand complexes. Chemical feature interaction based pharmacophore models extracted from these simulations, were recently used with consensus scoring approaches to identify potentially active molecules. While this approach is rapid and can be fully automated for virtual screening, additional relevant information from such simulations is still opaque and so far the full potential has not been entirely exploited. To address these aspects, we developed the hierarchical graph representation of pharmacophore models (HGPM). This single graph representation enables an intuitive observation of numerous pharmacophore models from long MD trajectories and further emphasizes their relationship and feature hierarchy. The resulting interactive depiction provides an easy-to-apprehend tool for the selection of sets of pharmacophores as well as visual support for analysis of pharmacophore feature composition and virtual screening results. Furthermore, the representation can be adapted to include information involving interactions between the same protein and multiple different ligands. Herein, we describe the generation, visualization and use of HGPMs generated from MD simulations of two x-ray crystallographic derived structures of the human glucokinase protein in complex with allosteric activators. The results demonstrate that a large number of pharmacophores and their relationships can be visualized in an interactive, efficient manner, unique binding modes identified and a combination of models derived from long MD simulations can be strategically prioritized for VS campaigns.

Layered shape matching and registration: Stochastic sampling with hierarchical graph representation

2008 ◽  
Author(s):  
Xiaobai Liu ◽  
Liang Lin ◽  
Hongwei Li ◽  
Hai Jin ◽  
Wenbing Tao
Keyword(s):  
Shape Matching ◽  
Graph Representation ◽  
Stochastic Sampling ◽  
Hierarchical Graph

Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning

2021 ◽  
pp. 283-292
Author(s):  
Chen Liu ◽  
Jinze Cui ◽  
Dailin Gan ◽  
Guosheng Yin
Keyword(s):  
Representation Learning ◽  
Graph Representation ◽  
Hierarchical Graph

scholarly journals Combining Attention Model with Hierarchical Graph Representation for Region-Based Image Retrieval

2008 ◽  
Vol E91-D (8) ◽  
pp. 2203-2206 ◽  
Author(s):  
S.-H. FENG ◽  
D. XU ◽  
B. LI
Keyword(s):  
Image Retrieval ◽  
Graph Representation ◽  
Attention Model ◽  
Hierarchical Graph
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