CD-Based Indices for Link Prediction in Complex Network

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.

A perturbation-based framework for link prediction via non-negative matrix factorization

“…Hence, relative precision is proposed to measure performances across different networks27. The random predictor is obtained by providing a ranking list that is ordered according to a random permutation of the links.…”

Predicting missing links in complex networks based on common neighbors and distance

“…Generally, between nodes with very high likelihood scores are considered to be highly likely to have missing links. In the past few years, many prediction methods based on topological structure of networks have been proposed related to local paths, common neighbors and random walk10111213. In social networks two individuals who have more common friends are very likely to be friends in future.…”

Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?

“…As a key intuition, Cannistraci et al postulated also that the identification of this form of learning in neuronal networks was only a special case; hence the epitopological learning and the associated local-community-paradigm (LCP) were proposed as local rules of learning, organization and link-growth valid in general for topological link prediction in any complex network with LCP architecture (Cannistraci et al 2013a ). On the basis of these ideas, they proposed a new class of link predictors that demonstrated - also in following studies of other authors - to outperform many state-of-the-art local-based link predictors (Cannistraci et al 2013a ; Liu et al 2013 ; Tan et al 2014 ; Pan et al 2016 ; Wang et al 2016 ; Wang et al 2016 ; Pech et al 2017 ; Shakibian and Charkari 2017 ) both in brain connectomes and in other types of complex networks (such as social, biological, economical, etc.). In addition, they proposed that the LCP is a necessary paradigm of network organization to trigger epitopological learning in any type of complex network, and that LCP-corr is a measure to quantitatively evaluate the extent to which a given complex network is organized according to the LCP.…”