32,074 research outputs found
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,
we 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. Our 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. After collapsing the tensor information, we
compute 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 nodes
relying on their closeness. We show the efficiency of this method by applying
it on three mobility traces: two real traces and one synthetic trace. Through
several simulations, we demonstrate the effectiveness of the technique
regarding another approach based on a similarity metric used in DTN. The
validity of this method is proven when the computation of score is made in a
distributed way (i.e. with local information). We attest that the tensor-based
technique is effective for temporal link prediction applied to the
intermittently connected networks. Furthermore, we think that this technique
can go beyond the realm of DTN and we believe this can be further applied on
every case of figure in which there is a need to derive the underlying social
structure of a network of mobile users.Comment: 13 pages, 9 figures, 8 tables, submitted to the International Journal
of Computer and Telecommunications Networking (COMNET
Discriminative Distance-Based Network Indices with Application to Link Prediction
In large networks, using the length of shortest paths as the distance measure
has shortcomings. A well-studied shortcoming is that extending it to
disconnected graphs and directed graphs is controversial. The second
shortcoming is that a huge number of vertices may have exactly the same score.
The third shortcoming is that in many applications, the distance between two
vertices not only depends on the length of shortest paths, but also on the
number of shortest paths. In this paper, first we develop a new distance
measure between vertices of a graph that yields discriminative distance-based
centrality indices. This measure is proportional to the length of shortest
paths and inversely proportional to the number of shortest paths. We present
algorithms for exact computation of the proposed discriminative indices.
Second, we develop randomized algorithms that precisely estimate average
discriminative path length and average discriminative eccentricity and show
that they give -approximations of these indices. Third, we
perform extensive experiments over several real-world networks from different
domains. In our experiments, we first show that compared to the traditional
indices, discriminative indices have usually much more discriminability. Then,
we show that our randomized algorithms can very precisely estimate average
discriminative path length and average discriminative eccentricity, using only
few samples. Then, we show that real-world networks have usually a tiny average
discriminative path length, bounded by a constant (e.g., 2). Fourth, in order
to better motivate the usefulness of our proposed distance measure, we present
a novel link prediction method, that uses discriminative distance to decide
which vertices are more likely to form a link in future, and show its superior
performance compared to the well-known existing measures
Tracking Topology Dynamicity for 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,
we 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. We attest that the tensor-based technique is effective for
temporal link prediction applied to the intermittently connected networks. The
validity of this method is proved when the prediction is made in a distributed
way (i.e. with local information) and its performance is compared to well-known
link prediction metrics proposed in the literature.Comment: Published in the proceedings of the 8th International Wireless
Communications and Mobile Computing Conference (IWCMC), Limassol, Cyprus,
201
Effective and Efficient Similarity Index for Link Prediction of Complex Networks
Predictions of missing links of incomplete networks like protein-protein
interaction networks or very likely but not yet existent links in evolutionary
networks like friendship networks in web society can be considered as a
guideline for further experiments or valuable information for web users. In
this paper, we introduce a local path index to estimate the likelihood of the
existence of a link between two nodes. We propose a network model with
controllable density and noise strength in generating links, as well as collect
data of six real networks. Extensive numerical simulations on both modeled
networks and real networks demonstrated the high effectiveness and efficiency
of the local path index compared with two well-known and widely used indices,
the common neighbors and the Katz index. Indeed, the local path index provides
competitively accurate predictions as the Katz index while requires much less
CPU time and memory space, which is therefore a strong candidate for potential
practical applications in data mining of huge-size networks.Comment: 8 pages, 5 figures, 3 table
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