Link prediction and classification in social networks and its application in healthcare and systems biology

Abstract

Prediction is one of the most attractive aspects in data mining. Link prediction has recently attracted the attention of many researchers as an effective technique to be used in graph based models in general and in particular for social network analysis due to the recent popularity of the field. Link prediction helps to understand associations between nodes in social communities. Existing link prediction-related approaches described in the literature are limited to predict links that are anticipated to exist in the future. To the best of our knowledge, none of the previous works in this area has explored the prediction of links that could disappear in the future. We argue that the latter set of links are important to know about; they are at least equally important as and do complement the positive link prediction process in order to plan better for the future. In this paper, we propose a link prediction model which is capable of predicting both links that might exist and links that may disappear in the future. The model has been successfully applied in two different though very related domains, namely health care and gene expression networks. The former application concentrates on physicians and their interactions while the second application covers genes and their interactions. We have tested our model using different classifiers and the reported results are encouraging. Finally, we compare our approach with the internal links approach and we reached the conclusion that our approach performs very well in both bipartite and non-bipartite graphs.

Social Network Link Prediction using Semantics Deep Learning

“…In [13] practice the SN which gives the best approach to seek and get customized, reliable health advice from peers at wherever and at any time, by tracing dental health information searched for got on Twitter. In [14], the authors proposed a link prediction model that can forecast links that may exist or vanish later.…”

Semantic-enhanced models to support timely admission prediction at emergency departments

“…In recent years, a number of data-mining techniques have been applied to health care and prediction models may be the most common applications (Almansoori et al 2012;Chan et al 2008). Our study is focused on models for predicting hospital admission.…”

Inter-variable correlation prediction with fuzzy connected-triples

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