describes the relational schema for the domain, and the probabilistic dependencies between attributes in the domain. A PRM, together with a particular database of entities and unobserved links, defines a probability distribution over the unobserved links. Probabilistic soft logic (PSL) Probabilistic soft logic (PSL) is a probabilistic graphical model over hinge-loss Markov random field (HL-MRF). HL-MRFs are created by a set of templated first-order logic-like rules, which are then grounded over the data. PSL can combine attribute, or local, information with topological, or relational, information. While PSL can incorporate local predictors, such as cosine similarity, it also supports relational rules, such as triangle completion in a network. Markov logic networks (MLNs) Markov logic networks (MLNs) is a probabilistic graphical model defined over Markov networks. These networks are defined by templated first-order logic-like rules, which is then grounded over the training data. MLNs are able to incorporate both local and relational rules for the purpose of link prediction. R-Model (RMLs) R-Models (RMLs) is a neural network model created to provide a deep learning approach to the link weight prediction problem. This model uses a node embedding technique that extracts node embeddings (knowledge of nodes) from the known links’ weights (relations between nodes) and uses this knowledge to predict the unknown links’ weights. Applications Link prediction has found varied uses, but any domain in which entities interact in a structures way can benefit from link prediction. A common applications of link prediction is improving similarity measures for collaborative filtering approaches to recommendation. Link prediction is also frequently used in social networks to suggest friends to users. It has also been used to predict criminal associations. In biology, link prediction has been used to predict interactions between proteins in protein-protein interaction networks. Link prediction has also been used to infer interactions between drugs and targets using link prediction Another application is found in collaboration prediction in scientific co-authorship networks. Entity resolution, also known as deduplication, commonly uses link prediction to predict whether two entities in a network are references to the same physical entity. Some authors have used context information in network structured domains to improve entity resolution. Link prediction in the context of network effects has been used to analyze tendencies to spread across networks and can be used to improve marketing strategies, in particular viral marketing. Software packages Free and open-source software Caffe CNTK Deeplearning4j DeepSpeed ELKI Infer.NET Keras Mahout Mallet