Mutual information model for link prediction in heterogeneous complex networks

Abstract

Recently, a number of meta-path based similarity indices like PathSim, HeteSim, and random walk have been proposed for link prediction in heterogeneous complex networks. However, these indices suffer from two major drawbacks. Firstly, they are primarily dependent on the connectivity degrees of node pairs without considering the further information provided by the given meta-path. Secondly, most of them are required to use a single and usually symmetric meta-path in advance. Hence, employing a set of different meta-paths is not straightforward. To tackle with these problems, we propose a mutual information model for link prediction in heterogeneous complex networks. The proposed model, called as Meta-path based Mutual Information Index (MMI), introduces meta-path based link entropy to estimate the link likelihood and could be carried on a set of available meta-paths. This estimation measures the amount of information through the paths instead of measuring the amount of connectivity between the node pairs. The experimental results on a Bibliography network show that the MMI obtains high prediction accuracy compared with other popular similarity indices.

Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes

“…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 8 . 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 8 , 47 – 53 both in brain connectomes and in other types of complex networks (such as social, biological, economical, etc.). In addition, they proposed that the local-community-paradigm is a necessary paradigm of network organization to trigger epitopological learning in any type of complex network, and that LCP-correlation 8 is a measure to quantitatively evaluate the extent to which a given complex network is organized according to the LCP.…”

Local-community network automata modelling based on length-three-paths for prediction of complex network structures in protein interactomes, food webs and more

“…As a key intuition, Cannistraci et al [3] 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 [3]. 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 [3]- [9], [11] both in brain connectomes and in other types of complex networks (such as social, biological, economical, etc.). In addition, they proposed that the local-community-paradigm is a necessary paradigm of network organization to trigger epitopological learning in any type of complex network.…”

“…Hence, the current name is misleading. Since this model is a generalization and reinterpretation of a Hebbian learning local-rule to create new topology in networks, we decide to rename CRA as L2-based Cannistraci-Hebb network automaton model number one (CH1-L2), rather than Cannistraci-Resource-Allocation (CRA), as it was named in the previous articles [3]- [9], [11]- [13]. Yet, we have to admit that the formula of CH1-L2 is conceptually and mathematically awkward.…”

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.…”