Big graph classification frameworks based on Extreme Learning Machine
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.11.035
Abstract: Abstract Graph data analysis is a hot topic in recent research area. Graph classification is one of the most important graph data analysis problems, which choose the most probable class labels of graphs using models… read more here.
Keywords: classification; graph data; big graph; graph classification ... See more keywords
Hierarchical Model Selection for Graph Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2023.3246128
Abstract: Node classification on graph data is a major problem in machine learning, and various graph neural networks (GNNs) have been proposed. Variants of GNNs such as H2GCN and CPF outperform graph convolutional networks (GCNs) by… read more here.
Keywords: hierarchical model; neural networks; model selection; graph neural ... See more keywords
Distributed and Privacy Preserving Graph Data Collection in Internet of Thing Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2021.3112186
Abstract: Internet of Thing (IoT) systems have been treated as a novel platform for graph data acquisition. Contents like dynamic network topology, organization and control flows, and interactions among monitored objects all contribute to the huge… read more here.
Keywords: internet thing; data collection; privacy; collection ... See more keywords
Maritime Target Detection Based on Radar Graph Data and Graph Convolutional Network
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2021.3133473
Abstract: Due to the complex sea clutter environment and target features, the conventional statistical theory-based methods cannot achieve high performance in maritime target detection tasks. Conventional deep learning, such as convolutional neural networks (CNNs)-based target detection… read more here.
Keywords: maritime target; detection; graph data; target detection ... See more keywords
De-SAG: On the De-Anonymization of Structure-Attribute Graph Data
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2017.2712150
Abstract: In this paper, we study the impacts of non-Personal Identifiable Information (non-PII) on the privacy of graph data with attribute information (e.g., social networks data with users’ profiles (attributes)), namely Structure-Attribute Graph (SAG) data, both… read more here.
Keywords: anonymization; graph; graph data; structure attribute ... See more keywords
GPENs: Graph Data Learning With Graph Propagation-Embedding Networks.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3120100
Abstract: Compact representation of graph data is a fundamental problem in pattern recognition and machine learning area. Recently, graph neural networks (GNNs) have been widely studied for graph-structured data representation and learning tasks, such as graph… read more here.
Keywords: representation; propagation; graph propagation; propagation embedding ... See more keywords
Optimal Representation of Large-Scale Graph Data Based on Grid Clustering and K2-Tree
Sign Up to like & getrecommendations! Published in 2020 at "Mathematical Problems in Engineering"
DOI: 10.1155/2020/2354875
Abstract: The application of appropriate graph data compression technology to store and manipulate graph data with tens of thousands of nodes and edges is a prerequisite for analyzing large-scale graph data. The traditional K2-tree representation scheme… read more here.
Keywords: large scale; graph data; scale graph; graph ... See more keywords
BED: a Biological Entity Dictionary based on a graph data model.
Sign Up to like & getrecommendations! Published in 2018 at "F1000Research"
DOI: 10.12688/f1000research.13925.2
Abstract: The understanding of molecular processes involved in a specific biological system can be significantly improved by combining and comparing different data sets and knowledge resources. However, these information sources often use different identification systems and… read more here.
Keywords: graph data; biological entity; graph; data model ... See more keywords
Understanding the Variability in Graph Data Sets through Statistical Modeling on the Stiefel Manifold
Sign Up to like & getrecommendations! Published in 2021 at "Entropy"
DOI: 10.3390/e23040490
Abstract: Network analysis provides a rich framework to model complex phenomena, such as human brain connectivity. It has proven efficient to understand their natural properties and design predictive models. In this paper, we study the variability… read more here.
Keywords: data sets; graph data; variability; understanding variability ... See more keywords
The Self-Information Weighting-Based Node Importance Ranking Method for Graph Data
Sign Up to like & getrecommendations! Published in 2022 at "Entropy"
DOI: 10.3390/e24101471
Abstract: Due to their wide application in many disciplines, how to make an efficient ranking for nodes, especially for nodes in graph data, has aroused lots of attention. To overcome the shortcoming that most traditional ranking… read more here.
Keywords: information; self information; information weighting; weighting based ... See more keywords