Multimorbidity, frequently associated with aging, can be operationally defined as the presence of two or more chronic conditions. Predicting the likelihood of a patient with multimorbidity to develop a further particular disease in the future is one of the key challenges in multimorbidity research. In this paper we are using a network-based approach to analyze multimorbidity data and develop methods for predicting diseases that a patient is likely to develop. The multimorbidity data is represented using a temporal bipartite network whose nodes represent patients and diseases and a link between these nodes indicates that the patient has been diagnosed with the disease. Disease prediction then is reduced to a problem of predicting those missing links in the network that are likely to appear in the future. We develop a novel link prediction method for static bipartite network and validate the performance of the method on benchmark datasets. By using a probabilistic framework, we then report on the development of a method for predicting future links in the network, where links are labelled with a time-stamp. We apply the proposed method to three different multimorbidity datasets and report its performance measured by different performance metrics including AUC, Precision, Recall, and F-Score.

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Multimorbidity prediction using link prediction.

Probabilistic Spin Logic (PSL) is a recently proposed computational paradigm implemented using unstable stochastic units called probabilistic bits, or pbits. The stochastic nature of the pbits is responsible for its unique invertibility property. In this paper, we have exploited the invertibility property to complete a partial image. The digit image set consists of ten images of digits 0 to 9 of size $5 \times 3$ pixels for training the PSL network. Results show that the fully connected PSL (FC-PSL) network successfully recovers the unclamped pixels for all the digits. Moreover, we propose an area-efficient sparsely connected PSL (SC-PSL) network with ~ 42% of the original connections. First, we perform the sparsification on an FC-PSL network using the conventional weight pruning methods. Next, we propose a method to predict the SC-PSL network structure using mutual information based on the perspective of information theory. 

Image Completion using a Sparse Probabilistic Spin Logic Network

Probabilistic Spin Logic (PSL) is a recently proposed computational paradigm implemented using unstable stochastic units called probabilistic bits, or pbits. The stochastic nature of the pbits is responsible for its unique invertibility property. In this paper, we have exploited the invertibility property to complete a partial image. The digit image set consists of ten images of digits 0 to 9 of size $5 \times 3$ pixels for training the PSL network. Results show that the fully connected PSL (FC-PSL) network successfully recovers the unclamped pixels for all the digits. Moreover, we propose an area-efficient sparsely connected PSL (SC-PSL) network with ~ 42% of the original connections. First, we perform the sparsification on an FC-PSL network using the conventional weight pruning methods. Next, we propose a method to predict the SC-PSL network structure using mutual information based on the perspective of information theory.

: Many data can be easily modelled as bipartite or k-partite graphs. Among the many computational analyses that can be run on such graphs, link prediction, i.e., the inference of novel links between nodes, is one of the most valuable and has many applications on real world data. While for bipartite graphs many methods exist for this task, only few algorithms are able to perform link prediction on k-partite graphs. The Probabilistic Latent Semantic Analysis (PLSA) is an algorithm based on latent variables, named topics, designed to perform matrix factorisation. As such, it is straightforward to apply PLSA to the task of link prediction on bipartite graphs, simply by decomposing the association matrix. In this work we extend PLSA to k-partite graphs; in particular we designed an algorithm able to perform link prediction on k-partite graphs, by exploiting the information in all the layers of the target graph. Our experiments confirm the capability of the proposed method to effectively perform link prediction on k-partite graphs.

Generalization of Probabilistic Latent Semantic Analysis to k-partite Graphs

Many real world person-person or person-product relationships can be modeled graphically. More specifically, bipartite graphs can be especially useful when modeling scenarios that involve two disjoint groups. As a result, many existing papers have utilized bipartite graphs for the classical link recommendation problem. In this paper, using the principle of bipartite graphs, we present another approach to this problem with a two step algorithm that takes into account frequency and similarity between common edges to make recommendations. We test this approach with bipartite data gathered from the Epinions and Movielens data sources, and find it to perform with roughly 14 percent error, which improves upon baseline results. This is a promising result, and can be refined to generate even more accurate recommendations.

A Two Step Approach to Weighted Bipartite Link Recommendations

Link prediction in networks has applications in computer science, graph theory, biology, economics, etc. Link prediction is a very well studied problem. Out of all the different versions, link prediction for unipartite graphs has attracted most attention. In this work we focus on link prediction for bipartite graphs that is based on two very important concepts-potential energy and mutual information. In the three step approach; first the bipartite graph is converted into a unipartite graph with the help of a weighted projection, next the potential energy and mutual information between each node pair in the projected graph is computed. Finally, we present Potential Energy-Mutual Information based similarity metric which helps in prediction of potential links. To evaluate the performance of the proposed algorithm four similarity metrics, namely AUC, Precision, Prediction-power and Precision@K were calculated and compared with eleven baseline algorithms. The Experimental results show that the proposed method outperforms the baseline algorithms.

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A potential energy and mutual information based link prediction approach for bipartite networks.

Networks are everywhere and their applications in various fields such as computer science, biological science, economics, and chemical engineering attracted attention of many researchers. Many complex systems in the real world can be represented by networks or graphs. Link prediction is one of the most important tasks in network analysis, thus attracting tremendous research interests in the last decades. In this paper, we present a novel algorithm for link prediction that works efficiently for both unipartite graphs and bipartite graphs. Our novel algorithm is based on the concept of eigenvectors and shortest distance between the nodes. We used the Peron–Frobenius theorem of node importance for link prediction. Four metrics, namely AUC, precision, prediction-power, and precision@K, were computed and compared with fourteen baseline algorithms to test the performance of the proposed algorithm. Testing was done on the thirteen datasets, and experimental results show that the proposed method outperforms the baseline algorithm on the basis of four given metrics.

Purushottam Kumar

Papers

A potential energy and mutual information based link prediction approach for bipartite networks.

A novel similarity measure for the link prediction in unipartite and bipartite networks