Abstract
Link prediction is to estimate the possibility of future links among nodes by utilizing known information such as network topology and node attributes. According to the characteristics of opportunistic networks (topological time-variation, node mobility and intermittent connections), this paper proposes a novel link prediction approach (IRWR-DBN) for opportunistic networks that is based on random walk and a deep belief network. First, we reconstruct the Markov probability transition matrix and define a similarity index- improved random walk with restart (IRWR). Second, we divide the opportunistic network into network snapshots. Then, the similarity matrix of each snapshot is calculated by using the IRWR index to construct a sample set. Finally, a predictive model is constructed based on a deep belief network which extracts the time-domain characteristics in the process of dynamic evolution of the opportunistic network. The experimental results on the ITC and MIT Reality datasets show that compared with methods, such as the similarity-based index (CN, AA, Katz, RA, RWR), convolutional neural network, and recurrent neural network, the proposed method is more accurate and stable.
Highlights
Opportunistic networks [1] are mobile ad hoc networks that do not require an end-to-end link between the source node and the target node and establish communication through the movement of network nodes
We propose a link prediction model based on improved random walk with restart (IRWR) and deep belief network (IRWRDBN), which can extract the time-domain characteristics in the process of dynamic evolution of the opportunistic networks
RELATED WORK Link prediction methods of opportunistic networks fall into the following categories: predictions based on similarity indexes, matrix decomposition, and machine learning
Summary
Opportunistic networks [1] are mobile ad hoc networks that do not require an end-to-end link between the source node and the target node and establish communication through the movement of network nodes. Z. Liao et al.: Novel Link Prediction Method for Opportunistic Networks Based on Random Walk and a Deep Belief Network possibility of future links among nodes by utilizing known information, such as network topology and node attributes. Most existing link prediction methods are proposed for static networks or social networks where the network topology does not change or changes slowly over time. These methods do not sufficiently apply temporal information. We propose a link prediction model based on IRWR and deep belief network (IRWRDBN), which can extract the time-domain characteristics in the process of dynamic evolution of the opportunistic networks.
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