Abstract
Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks.
Highlights
In our real world, many complex systems including social, biological, information and technology can be well described by networks where nodes represent individuals or agents, and links denote relations or interactions between nodes
The proposed Cosine Distance Index (CD)-based algorithms and fifteen existing methods were compared on ten real networks, and their Precision values are shown in Table 2 and the best value of each network is emphasized by boldface
Fifteen existing similarity indices are compared with the proposed indices and experimental results demonstrate the effectiveness of CD-based indices
Summary
Many complex systems including social, biological, information and technology can be well described by networks where nodes represent individuals or agents, and links denote relations or interactions between nodes. In some networks, such as in protein-protein interaction networks [1,2], electrical power grid [3] and air transportation networks [4], how can we find out which pair of entities likely generate new links in the near future? More applications of link prediction, please reference to [5,6,14]
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