Application of graph learning with multivariate relational representation matrix in vehicular social networks
The essence of connection in vehicle network is the social relationship between people, and
thus Vehicular Social Networks (VSNs), characterized by social aspects and features, can …
thus Vehicular Social Networks (VSNs), characterized by social aspects and features, can …
A method of vehicle route prediction based on social network analysis
N Ye, Z Wang, R Malekian, Y Zhang… - Journal of …, 2015 - Wiley Online Library
A method of vehicle route prediction based on social network analysis is proposed in this
paper. The difference from proposed work is that, according to our collected vehicles' past …
paper. The difference from proposed work is that, according to our collected vehicles' past …
AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks
With well-defined graphs, graph convolution based spatiotemporal neural networks for traffic
prediction have achieved great performance in numerous tasks. Compared to other …
prediction have achieved great performance in numerous tasks. Compared to other …
Multivariate relations aggregation learning in social networks
Multivariate relations are general in various types of networks, such as biological networks,
social networks, transportation networks, and academic networks. Due to the principle of …
social networks, transportation networks, and academic networks. Due to the principle of …
Multi-stage attention spatial-temporal graph networks for traffic prediction
Accurate traffic prediction plays an important role in Intelligent Transportation System. This
problem is very challenging due to the heterogeneity and dynamic spatio-temporal …
problem is very challenging due to the heterogeneity and dynamic spatio-temporal …
Adaptive Spatial-Temporal Graph Convolution Networks for Collaborative Local-Global Learning in Traffic Prediction
The rapid growth of vehicles as countries become more developed has brought great
challenges to traffic prediction. Recent works model only local or global spatial-temporal …
challenges to traffic prediction. Recent works model only local or global spatial-temporal …
A graph neural network approach for product relationship prediction
Graph representation learning has revolutionized many artificial intelligence and machine
learning tasks in recent years, ranging from combinatorial optimization, drug discovery …
learning tasks in recent years, ranging from combinatorial optimization, drug discovery …
MCNE: An end-to-end framework for learning multiple conditional network representations of social network
Recently, the Network Representation Learning (NRL) techniques, which represent graph
structure via low-dimension vectors to support social-oriented application, have attracted …
structure via low-dimension vectors to support social-oriented application, have attracted …
Offer: A motif dimensional framework for network representation learning
Aiming at better representing multivariate relationships, this paper investigates a motif
dimensional framework for higher-order graph learning. The graph learning effectiveness …
dimensional framework for higher-order graph learning. The graph learning effectiveness …
RGDAN: A random graph diffusion attention network for traffic prediction
Traffic Prediction based on graph structures is a challenging task given that road networks
are typically complex structures and the data to be analyzed contains variable temporal …
are typically complex structures and the data to be analyzed contains variable temporal …