Automatic Virtual Network Embedding: A Deep Reinforcement Learning Approach With Graph Convolutional Networks.
Zhongxia YanJingguo GeYulei WuLiangxiong LiTong LiPublished in: IEEE J. Sel. Areas Commun. (2020)
Keyphrases
- reinforcement learning
- network structure
- complex networks
- sparsely connected
- dynamic networks
- fully connected
- small world
- overlapping communities
- highly connected
- network size
- protein interaction networks
- link formation
- network parameters
- network topologies
- network model
- network design
- community structure
- community discovery
- computer networks
- average degree
- scale free
- clustering coefficient
- degree distribution
- real world social networks
- social networks
- citation networks
- heterogeneous networks
- network architecture
- directed edges
- functional modules
- real world networks
- telecommunication networks
- graphical representation
- function approximation
- graph theory
- cellular networks
- transport network
- link prediction
- network nodes
- deep learning
- connectionist networks
- parity check
- graph mining algorithms
- heterogeneous social networks
- virtual environment
- directed graph
- network properties
- bipartite graph
- spanning tree
- graph embedding
- biological networks
- network resources
- path length
- densely connected
- random graphs
- random graph models
- learning algorithm
- wireless sensor networks
- peer to peer
- random walk
- information diffusion
- internet traffic
- social network analysis
- power law
- attack graph
- deep belief networks
- network evolution
- network analysis