Fig. 2: Diagram of the five major categories of link prediction methods.
From: Assessment of community efforts to advance network-based prediction of protein–protein interactions
(1) Similarity-based methods. These methods quantify the likelihood of links based on predefined similarity functions among nodes in the graph, i.e., the common neighbors (green area). (2) Probabilistic methods. These methods assume that real networks have some structure, e.g., community structure. The goal of these algorithms is to select model parameters that can maximize the likelihood of the observed structure. The connecting probability of nodes within a community is higher than that between different communities (gray matrix). (3) Factorization-based: The goal of these methods is to learn a lower dimensional representation for each node in the graph by preserving the global network patterns. Next, the compressed representation is leveraged to predict unobserved PPIs by either calculating a similarity function or training a classifier. (4) Machine learning: There are numerous methods among machine learning categories; here, we illustrate this category using the state-of-the-art graph neural networks (GNN). Those methods embed node information by aggregating the node features, link features and graph structure using a neural network and passing the information through links in the graph. Thereafter, the learned representations are used to train a supervised model to predict the missing links. (5) Diffusion-based: These methods use techniques based on the analysis of the information gleaned from the movement of a random walker diffusion over the network (paths indicated by red arrows).
