# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 5 Discussion and Conclusions  
Link prediction problems in social networks gained considerable research interest from various domains including anthropology, sociology, biology, information and computer science. Focusing on static network topological information without considering the influence of evolutionary process and associated dynamic changes incident to all actors in the temporal network, has led the existing methods to be incompetent in dynamic link prediction despite their compliance with the performance expectations. Recently, scholars tend to acknowledge that emerging links can be derived by mining the evolutionary information extracted from the network snapshots over time. Dynamic network topology along with associated evolutionary information resulting from the temporal, structural, and neighbourhood changes, associated with individual actors, can be exploited in dynamic link prediction. Furthermore, since most networks inherently evolve over time, it is imperative to exploit the temporal network dynamics to resolve issues with link prediction problems in dynamic networks. Considering the problem of dynamic link prediction, this study attempted to develop evolutionary features by measuring the temporal similarity and correlation of the actor-oriented evolutionary information in dynamic networks. For this purpose, we considered both directed and undirected social networks of different sizes and domains. In a dynamic network composition, each selected network was sampled by considering three  
Figure 3: P–R curves (**left column**) and ROC curves (**right column**) for undirected network \(G_{UCI}\) (**top row**), co-authorship network \(G_{th}\) (**middle row**), and directed network \(G_{Rust}\) (**bottom row**). different resolutions ranging from hours and days to years to generate SINs. Considering different temporal granularity, this study then developed a time series of structural and neighbourhood changes experienced by each user. Considering the rate of changes, this study then defined two temporal measures to quantify actors' temporal behaviour. These measures are defined as structural and neighbourhood dynamics. To develop the dynamic features, this study leveraged the evolution of temporal similarity, and the correlation of these two dynamicity values to quantify the similarity/proximity between actors from an evolutionary perspective. The first four dynamic similarity metrics were constructed in this way. The fifth dynamic feature was constructed by considering the similarity measures widely used in ecology. In this measure, we quantify the normalised abundance of actor-level dynamicity in temporal networks. In a supervised link prediction setup, we have used two ensemble-based classifiers and one linear classifier to measure the performance of our dynamic features. Through the performance metrics used in this study, we have observed that these features can not only be indulged for dynamic link prediction purposes but also can effectively support modelling the network growth. The performance of dynamic features was also compared with a traditional topological metric widely used for link prediction purposes in cross-sectional networks and a time series-based dynamic link prediction strategy. In both cases, we have observed that dynamic features constructed by leveraging the evolutionary aspect of actors can perform as well as the traditional ones and sometimes can outweigh them in regard to prediction performance. This study can further be extended in different ways. For example, instead of considering the network structural changes, one can exploit the temporal clustering tendency of actors, introduce time series forecasting methods to predict the future values of actor-level changes and employ other similarity measures (e.g., Euclidean, Manhattan) to measure the similarity between temporal information. One important observation we have noticed in this study is that the choice of the optimal sliding window to sample dynamic networks can effectively impact prediction performance.