Abstract
The link-prediction problem is an open issue in data mining and knowledge discovery, which attracts researchers from disparate scientific communities. A wealth of methods have been proposed to deal with this problem. Among these approaches, most are applied in unweighted networks, with only a few taking the weights of links into consideration. In this paper, we present a weighted model for undirected and weighted networks based on the mutual information of local network structures, where link weights are applied to further enhance the distinguishable extent of candidate links. Empirical experiments are conducted on four weighted networks, and results show that the proposed method can provide more accurate predictions than not only traditional unweighted indices but also typical weighted indices. Furthermore, some in-depth discussions on the effects of weak ties in link prediction as well as the potential to predict link weights are also given. This work may shed light on the design of algorithms for link prediction in weighted networks.
Highlights
The problem of link prediction attempts to uncover missing links and predict the emergence of future links in complex networks based on the available information, such as observed links and nodes’ attributes [1,2,3]
We give the comparison of our Weighted Mutual Information (WMI) model to the Local Naïve Bayes model (LNB) proposed in paper [18]
We propose a weighted mutual information model for link prediction in weighted networks, which combines the benefits from both structural properties and link weights
Summary
The problem of link prediction attempts to uncover missing links and predict the emergence of future links in complex networks based on the available information, such as observed links and nodes’ attributes [1,2,3]. With the overload of information nowadays, the dependence of people on information filtering systems, such as recommender systems, is increasing [8, 9]. In this sense, link prediction can serve as a significant technique in recommender systems, such as e-commerce recommendation [10] and friendship recommendation [11, 12]. The link-predictability problem was proposed to characterize the extent of links in a network could be predicted [15]. This can help us understand the organization of real networks
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