Link prediction in networks is typically accomplished by estimating or ranking the probabilities of edges for all pairs nodes. In practice, especially social networks, data are often collected egocentric sampling, which means selecting a subset nodes and recording their edges. This sampling mechanism requires different tools than typical assumption links missing at random. We propose new comput...

Predicting new links in physical, biological, social, or technological networks has a significant scientific and societal impact. Path-based link prediction methods utilize the explicit counting of even- odd-length paths between nodes to quantify score function infer unobserved links. Here, we propose quantum algorithm for path-based using controlled continuous-time walk encode even odd scores....

Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this article, we study both supervised and unsupervised link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heteroge...

The ability to predict linkages among data objects is central to many data mining tasks, such as product recommendation and social network analysis. A substantial literature has been devoted to the link prediction problem either as an implicitly embedded problem in specific applications or as a generic data mining task. This literature has mostly adopted a static graph representation where a sn...

Link prediction is an important task in Social Network Analysis. This problem refers to predicting the emergence of future relationships between nodes in a social network. Our work focuses on a supervised machine learning approach for link prediction. Here, the target attribute is a class label indicating the existence or absence of a link between a node pair. The predictor attributes are metri...