Exploiting Structural and Temporal Evolution in Dynamic Link Prediction

Link prediction in dynamic networks is an important task with many real-life applications in different domains, such as social networks, cyber-physical systems, and bioinformatics. There are two key processes in dynamic networks: network structural evolution and network temporal evolution, where the former represents interdependency between entities and their neighbors in the network at each timestamp, while the latter captures the evolving behavior of the entire network from the current timestamp to the next. Structural evolution generally assumes that a node is more likely to co-evolve with its neighbors in the near future. Temporal evolution focuses on the trend of network evolution as a whole, based on the accumulation of historical data. It is thus essential to use characteristics of both structural and temporal evolutions to emulate complex behaviors of dynamic networks. However, very few existing work considered both processes. In addition, real-life networks are often very sparse with limited observed links. A missing link between two nodes does not always imply that the two nodes do not have a relation in reality, especially when they share many common neighbors. Most existing methods only focus on the first-order proximity of networks, which is usually insufficient to capture the relationships among nodes.