Tensor-Based Link Prediction in Intermittently Connected Wireless Networks
Through several studies, it has been highlighted that mobility patterns in mobile networks are driven by human behaviors. This effect has been particularly observed in intermittently connected networks like DTN (Delay Tolerant Networks). Given that common social intentions generate similar human behavior, it is relevant to exploit this knowledge in the network protocols design, e.g. to identify the closeness degree between two nodes. In this paper, the authors propose a temporal link prediction technique for DTN which quantifies the behavior similarity between each pair of nodes and makes use of it to predict future links. Their prediction method keeps track of the spatio-temporal aspects of nodes behaviors organized as a third-order tensor that aims to records the evolution of the network topology.