# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 1 Introduction
Different dynamic link prediction methods explore a wide range of techniques. Most of the techniques used a wide variety of structural and network topological features to compute the likelihood of link formations. For example, [PERSON] et al. [19] used a node (i.e., actor) centrality-based temporal link prediction where the authors distinguished the contributions of common neighbors to connection likelihood by their eigenvector centralities. By considering the importance of nodes as the probability of attracting other nodes, [PERSON] et al. [20] used an eigenvector-based node ranking strategy along with a forecasting method called Adaptive Weighted Moving (AWM) for dynamic link prediction. [PERSON] et al. [21] categorized the nodes into different levels based on the influence strength of the node compared to its neighbors that change over time. The authors computed the connection probability between a pair of nodes using their corresponding levels of influence strength and the attraction force between them to predict the missing links in dynamic networks. [PERSON] and [PERSON] [22] formulated the link prediction problem in dynamic networks as an optimization problem that not only collectively leveraged the structural and temporal information to better infer a low-rank representation for each node but also preserved the deep network structure via high-order proximity among nodes. The authors also used an efficient block coordinate gradient descent approach to address the optimization problem. Among other methods, researchers exploited the collective influence, the community walk features, and the centrality features [23], subgraph evolution [24], effective influence mechanism in relation to actors' degree and strong connectivity across short and long path among them [25], 'graphlet' transitions [26], dynamic latent space representation of actors and random walk in temporal networks [27], the correlation between different types of links along with temporal features (e.g., \"recency\", temporal activeness) [28], probabilistic temporal measure [29], probabilistic generative model [30], matrix and tensor factorization [31] and deep learning techniques [32]. Nevertheless, some of them are subject to their inherent limitation. For example, probabilistic models involve the prior definition of link occurrences' distribution, which is problematic for temporal networks. Furthermore, the probabilistic model is only suitable for small networks with a few hundred actors. Similarly, matrix or tensor-based methods are not feasible for real-time link predictions in large networks due to the computational complexity and time requirements [33]. Supervised link prediction techniques in dynamic networks [34; 35] take advantage of either temporal sequences or temporal variations of several network topological properties (e.g., Commonwealth, Jaccard, AdamicAdar), used to measure similarity/proximity between actor pairs in static networks, to train the classifier instead of measuring their similarity/proximity by mining actor-level evolutionary aspects including temporal patterns of neighbourhood changes or evolutionary community-aware information. Some of these techniques [36; 37] included a time series forecasting method to predict the future values of topological changes to training classifiers for supervised link prediction. This exercise can be counterproductive since the prediction is performed using predicted and unrealistic values. To address these issues, this study attempted to define dynamic features by employing temporal similarity and correlation of actor-level evolution (e.g., temporal structural and neighbourhood changes) experienced by individual actors in dynamic networks. Actors in dynamic networks are subject to varying temporal changes (i.e., dynamicity) within the temporal network snapshots due to variations of different network activities (e.g., link formation, link deletion) over time. This leads to temporal changes in actors' positions and neighbourhoods in dynamic networks.