the sum of the log of the intersection of the neighbors of two nodes. This captures a two-hop similarity, which can yield better results than simple one-hop methods. It is computed as follows: where is the set of nodes adjacent to . Katz measure Neighbor based methods can be effective when the number of neighbors is large, but this is not the case in sparse graphs. In these situations it is appropriate to use methods that account for longer walks. The Katz Measure is one metric that captures this. It is computed by searching the graph for paths of length in the graph and adding the counts of each path length weighted by user specified weights. Let A be the adjacency matrix of a network under consideration. Elements of A are variables that take a value 1 if a node i is connected to node j and 0 otherwise. The powers of A indicate the presence (or absence) of links between two nodes through intermediaries. For instance, in matrix , if element , it indicates that node 2 and node 12 are connected through some walk of length 3. If denotes Katz centrality of a node i, then mathematically: Note that the above definition uses the fact that the element at location of reflects the total number of degree connections between nodes and . Node attribute-based methods Node-similarity methods predict the existence of a link based on the similarity of the node attributes. Euclidean distance The attribute values are represented as normalized vector and the distance between the vectors used to measure similarity. Small distances indicate higher similarity. Cosine similarity After normalizing the attribute values, computing the cosine between the two vectors is a good measure of similarity, with higher values indicating higher similarity. Mixed methods Mixed methods combine attribute and topology based methods. Graph embeddings Graph embeddings also offer a convenient way to predict links. Graph embedding algorithms, such as Node2vec, learn an embedding space in which neighboring nodes are represented by vectors so that vector similarity measures, such as dot product similarity, or euclidean distance, hold in the embedding space. These similarities are functions of both topological features and attribute-based similarity. One can then use other machine learning techniques to predict edges on the basis of vector similarity. Probabilistic relationship models A probabilistic relational model (PRM) specifies a template for a probability distribution over a databases. The template