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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. Our prediction method keeps track of the spatio-temporal aspectsof nodes behaviors organized as a third-order tensor that aims to records theevolution of the network topology. After collapsing the tensor information, wecompute the degree of similarity for each pair of nodes using the Katz measure.This metric gives us an indication on the link occurrence between two nodesrelying on their closeness. We show the efficiency of this method by applyingit on three mobility traces: two real traces and one synthetic trace. Throughseveral simulations, we demonstrate the effectiveness of the techniqueregarding another approach based on a similarity metric used in DTN. Thevalidity of this method is proven when the computation of score is made in adistributed way (i.e. with local information). We attest that the tensor-basedtechnique is effective for temporal link prediction applied to theintermittently connected networks. Furthermore, we think that this techniquecan go beyond the realm of DTN and we believe this can be further applied onevery case of figure in which there is a need to derive the underlying socialstructure of a network of mobile users.

Tensor-Based Link Prediction in Intermittently Connected Wireless Networks