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Mixed Graph Neural Network-Based Fake News Detection for Sustainable Vehicular Social Networks
IEEE transactions on intelligent transportation systems (Print), 2023The rapid development of the Internet of Vehicles has substantially boosted the prevalence of vehicular social networks (VSN). However, content security has gradually been a latent threat to the stable operation of VSN.
Zhiwei Guo +5 more
semanticscholar +1 more source
Application of mixed graph traversal optimization for the vehicle routing problem
European Control Conference, 2022The Vehicle routing problem (VRP) is widely discussed in the literature. The goal of the VRP is to provide an optimized traversal of a graph. This paper presents a mixed graph traversal optimization for the VRP.
László Kocsány, E. Szádeczky-Kardoss
semanticscholar +1 more source
Demixing and Topology Identification for Mixed Graph Signals
International Conference Communication and Information Systems, 2021Graph learning (GL) plays a pivotal role in graph signal processing (GSP) for inferring the underlying signal structure. However, most of the recent works only focus on the single-graph learning situation.
Jian Hong, Xuchu Dai
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Multi-stream mixed graph convolutional networks for skeleton-based action recognition
J. Electronic Imaging, 2021. The skeleton-based action recognition task has recently drawn much attention in computer vision, and graph convolutional networks (GCNs) have shown great advantages in this task. However, in shallow layers of GCNs, the sharing of convolution kernels in
Boyuan Zhuang +3 more
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A self‐stabilizing algorithm for constructing a maximal (σ,τ)‐directed acyclic mixed graph
Concurrency and Computation, 2020A (σ,τ)‐directed acyclic mixed graph (DAMG) is a mixed graph, which allows both arcs (or directed edges) and (undirected) edges such that there exist exactly σ source nodes and τ sink nodes, but there exists no directed cycle (consisting of only arcs ...
Yonghwan Kim, Y. Katayama, T. Masuzawa
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Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design
Computers and Chemical Engineering, 2023ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution of machine learning certification problems.
Tom McDonald +3 more
semanticscholar +1 more source