Link Prediction in Social Networks

Srinivas, Virinchi; Mitra, Pabitra

doi:10.1007/978-3-319-28922-9

Cited by 21 publications

(2 citation statements)

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“…We simply refer to the AA algorithm as the best predictor because of its superior performance in other link prediction research studies. 19,32,33 Selective MLP performed with the highest matching percentage. However, the performance of the Selective Prediction algorithm was comparable (often similar) to the performance of the NLP First and PLP First algorithms.…”

Section: Resultsmentioning

confidence: 97%

Predicting brain network changes in Alzheimer's disease with link prediction algorithms

et al. 2017

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Link prediction is a promising research area for modeling various types of networks and has mainly focused on predicting missing links. Link prediction methods may be valuable for describing brain connectivity, as it changes in Alzheimer’s disease (AD) and its precursor, mild cognitive impairment (MCI). Here, we analyzed 3-tesla whole-brain diffusion-weighted images from 202 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) – 50 healthy controls, 72 with earlyMCI (eMCI) and 38 with lateMCI (lMCI) and 42 AD patients. We introduce a novel approach for Mixed Link Prediction (MLP) to test and define the percent of predictability of each heightened stage of dementia from its previous, less impaired stage, in the simplest case. Using well-known link prediction algorithms as the core of MLP, we propose a new approach that predicts stages of cognitive impairment by simultaneously adding and removing links in the brain networks of elderly individuals. We found that the optimal algorithm, called “Adamic and Adar”, had the best fit and most accurately predicted the stages of AD from their previous stage. When compared to the other link prediction algorithms, that mainly only predict the added links, our proposed approach can more inclusively simulate the brain changes during disease by both adding and removing links of the network. Our results are also in line with computational neuroimaging and clinical findings and can be improved for better results.

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Section: Resultsmentioning

confidence: 97%

Predicting brain network changes in Alzheimer's disease with link prediction algorithms

et al. 2017

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“…Link prediction evaluates the relationship between nodes and links in any network. It has a wide range of applications, including in social networks, protein–protein interactions, disease networks, transportation networks, drug target Interactions and so on (Srinivas & Mitra, 2016). We had performed a case study on the relationship between filmmakers and film critics.…”

Section: Introductionmentioning

confidence: 99%

Shabaz–Urvashi Link Prediction (SULP): A Novel Approach to Predict Future Friends in a Social Network

Shabaz

Garg

2020

Journal of Creative Communications

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With the growth of social networks, the problem of linking the isolated or missing nodes appears. Thus, link prediction comes into existence to resolve this problem. Link prediction may be defined as an approach to predict an optimistic relationship that may exist or is likely to exist between nodes. Predicting the prospect link formed in future between nodes either in a dense or sparse network, the number of techniques exist intending to establish a link based on a certain similarity between the nodes. After conducting in-depth research on almost every link prediction technique, we reach the conclusion that every technique evaluates the probability score to predict future links. This research work discusses almost every previous technique and puts forward a comparatively similar technique for link prediction. The proposed technique is named Shabaz–Urvashi Link Prediction (SULP), which is based on a formula derived from an empirical theory after making a node matrix and altering the position of the neighbouring nodes, which states, ‘A node is predicted to establish a friendship if it has a maximum degree in its common neighbouring row and a minimum degree in its common neighbouring column’. SULP is tested using established datasets and compared with other link prediction techniques on the statistical measures such as Area Under Receiver Operating characteristic Curve (AUROC), precision and recall. SULP performs better as compared to other link prediction techniques on most of the testing datasets.

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