Link Prediction with Social Vector Clocks
This work addresses link prediction in social networks by offering a more efficient method, though it appears incremental as it builds on existing concepts.
The paper tackled link prediction by introducing computationally cheaper features based on social vector clocks, which achieved the same performance as state-of-the-art methods and, when combined with previous approaches, yielded the most accurate predictor to date.
State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vector clocks, an adaptation of the vector-clock concept introduced in distributed computing to social interaction networks. In fact, our experiments suggest that by taking into account the order and spacing of interactions, social vector clocks exploit different aspects of link formation so that their combination with previous approaches yields the most accurate predictor to date.