An axiomatic approach to link prediction

Link prediction functions are important tools that are used to predict the evolution of a network, to locate hidden or surprising links, and to recommend new connections that should be formed. Multiple link prediction functions have been developed in the past. However, their evaluation has mostly been based on experimental work, which has shown that the quality of a link prediction function varies significantly depending on the input domain. There is currently very little understanding of why and how a specific link prediction function works well for a particular domain. The underlying foundations of a link prediction function are often left informal - each function contains implicit assumptions about the dynamics of link formation, and about structural properties that result from these dynamics. We draw upon the motivation used in characterizations of ranking algorithms, as well as other celebrated results from social choice, and present an axiomatic basis for link prediction. This approach seeks to deconstruct each function into basic axioms, or properties, that make explicit its underlying assumptions. Our framework uses "property templates" that can be considered as general choices made by a function designer, such as what score is assigned to a 2-vertex graph, which vertices are irrelevant to the score, how removing edges or contracting vertices affects the score, and more. Using this framework, we fully characterize four well known link prediction functions and show that they are in fact derived from different variants of a single basic set of property templates.