# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 1 Introduction  
Systems, regardless of being physical or abstract, in many real-world domains, including sociology, biology, criminology, informatics, economics and communication, can be mapped into a network. In these networks, nodes represent the individuals or actors, and links or edges represent various types of relations or interactions among them. These systems are inherently dynamic, where evolution occurs due to concurrent arrivals and/or removals of actors and simultaneously forming, strengthening, weakening, and dissolving ties among network actors over time. Network science caters to various methods supporting the study and modeling of a network evolution process that governs their dynamics [1]. Among them, link prediction is the fundamental computational problem that models the underlying growth mechanism of evolving networks [2]. The primary objective of link prediction methods is to estimate the likelihood of the emergence of new links among network actors utilizing the observed links, actor attributes, network structure, topology, or nodal properties [3]. This likelihood is measured in regard to similarity or proximity between non-connected node pairs, predominantly calculated using topological properties or probabilistic models [4]. The link prediction mechanism in social networks has gained considerable interest not only for mining and analyzing the network evolution in particular but also due to its wide variety of applications. These include recommendation systems, anomaly detection, influence analysis, and community detection [5], predicting linkage patterns in scientific collaboration networks [6], social security networks [7], disease spreading networks, especially the COVID-19 contact network [8], identifying hidden groups interrorist or criminal networks [9], discovering new protein interactions [10], understanding connectome patterns (mapping of neural connections in the brain) of organism's nervous system [11], improving transportation efficiency by efficient routing strategy [12], predicting users' ad-clicking actions and recommending interesting web contents for marketing purpose, and in sensor networks, ensuing information transfer secrecy [13] and optimal routing [14]. Subsequently, network science communities have proposed a wide range of similarity metrics and prediction strategies [15]. However, a vast magnitude of them focuses on static networks. There are two major hindrances to these prediction methods. Firstly, they depend on feature engineering over actors' network and non-network-based attributes by most of the classification and regression methods utilized for supervised and unsupervised link prediction [16]. Secondly, they do not acknowledge the dynamicity of a network resulting from changes in its past behaviours over time [17]. Although link prediction is a time-evolving network analysis model that measures the probability of future links by analyzing the existing links in the network, traditional similarity metrics-based methods generally overlook taking the evolutionary information of dynamic networks into account. Dynamic networks evolve through simultaneous arrivals and/or departure of actors as well as the creation and/or deletion of links among these actors. These time-varying networks, also known as \"temporal\" or \"longitudinal\" networks, are suitable for describing entities whose dynamics change over time. The combination of the technical possibility of storing, processing, and representing large-scale datasets and the increased proliferation and ubiquity of real-world dynamic network datasets has led dynamic network analysis to gain considerable research interest to understand the underlying mechanisms of their evolutionary dynamics better. Recently, researchers have attempted the issue of _dynamic link prediction_ or link prediction in dynamic networks. Dynamic link prediction is the process of inferring the possibility of future links among the dynamic entities or network actors through exploring historical or temporal information [18].