Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.

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.

Various structural features of networks have been applied to develop link prediction methods. However, because different features highlight different aspects of network structural properties, it is very difficult to benefit from all of the features that might be available. In this paper, we investigate the role of network topology in predicting missing links from the perspective of information theory. In this way, the contributions of different structural features to link prediction are measured in terms of their values of information. Then, an information-theoretic model is proposed that is applicable to multiple structural features. Furthermore, we design a novel link prediction index, called Neighbor Set Information (NSI), based on the information-theoretic model. According to our experimental results, the NSI index performs well in real-world networks, compared with other typical proximity indices.

Link weights are essential to network functionality, so weight prediction is important for understanding weighted networks given incomplete real-world data. In this work, we develop a novel method for weight prediction based on the local network structure, namely, the set of neighbors of each node. The performance of this method is validated in two cases. In the first case, some links are missing altogether along with their weights, while in the second case all links are known and weight information is missing for some links. Empirical experiments on real-world networks indicate that our method can provide accurate predictions of link weights in both cases.

With the establishment of intelligent transportation systems (ITS), research on vehicle ad-hoc networks (VANETs) has played an irreplaceable role in improving traffic safety and efficiency. However, because the deployment of devices based on the IoV is in an open field, the IoV is extremely vulnerable to various attacks without security protection, e.g., remote intrusion, control, trajectory tracking, etc. In order to avoid the above-mentioned attacks and resource abuses, provably secure cryptography primitives are generally considered to guarantee and realize the security of VANETs. This paper proposes a TPM-based conditional privacy-preserving authentication protocol (T-CPPA) which achieves both the integrity and the authenticity of the message/instruct content. The vehicle’s privacy is protected by embedding the system master private key into the trust platform module (TPM) which is responsible for generating pseudonyms and signature keys. The authenticity of message content is ensured by calculating message similarity in a cluster-based model. We give the concrete construction of our T-CPPA authentication scheme in symmetric bilinear groups and design a batch validation algorithm to improve efficiency. Security analysis shows that our scheme can resist various traditional attacks in VANETs, and the experimental results indicate that our scheme is efficient and useful in practice.