ArticleScientific reports2017
Mutual information model for link prediction in heterogeneous complex networks.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 56 citations in OpenAlex.
- An information theoretic approach to link prediction in multiplex networks.Scientific reports · 2021Article
- A potential energy and mutual information based link prediction approach for bipartite networks.Scientific reports · 2020Article
- Simplifying Weighted Heterogeneous Networks by Extracting h-Structure via s-Degree.Scientific reports · 2019Article
- Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models.F1000Research · 2019Article
- Adverse Drug Reaction Predictions Using Stacking Deep Heterogeneous Information Network Embedding Approach.Molecules (Basel, Switzerland) · 2018Article
- Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.Briefings in bioinformatics · 2018Article
- Similarity-based future common neighbors model for link prediction in complex networks.Scientific reports · 2018Article
- Modelling Self-Organization in Complex Networks Via a Brain-Inspired Network Automata Theory Improves Link Reliability in Protein Interactomes.Scientific reports · 2018Article
- Information Spread and Topic Diffusion in Heterogeneous Information Networks.Scientific reports · 2018Article
- Can local-community-paradigm and epitopological learning enhance our understanding of how local brain connectivity is able to process, learn and memorize chronic pain?Applied network science · 2017Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.