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Through several studies, it has been highlighted that mobility patterns inmobile networks are driven by human behaviors. This effect has beenparticularly observed in intermittently connected networks like DTN (DelayTolerant Networks). Given that common social intentions generate similar humanbehavior, it is relevant to exploit this knowledge in the network protocolsdesign, e.g. to identify the closeness degree between two nodes. In this paper,we propose a temporal link prediction technique for DTN which quantifies thebehavior similarity between each pair of nodes and makes use of it to predictfuture links. We attest that the tensor-based technique is effective fortemporal link prediction applied to the intermittently connected networks. Thevalidity of this method is proved when the prediction is made in a distributedway (i.e. with local information) and its performance is compared to well-knownlink prediction metrics proposed in the literature.

Tracking Topology Dynamicity for Link Prediction in Intermittently Connected Wireless Networks