Learning distributed representations for large scale dynamic social networks
Learning distributed representations of symbolic data were introduced by Hinton[1] and first developed in modeling networks for learning the node vectors by Perozzi et al (2014) In this work, we proposed Dnps, a novel nodes embedding approach for acquiring distributed representations of large scale dynamic social networks. Dnps is suitable for many types of social networks: dynamic/static, directed/undirected, and weighted/unweighted. Recently, several works of nodes embedding were proposed. However, they were designed for static networks, such as language networks. To address this problem, first, we develop a damping based positive sampling (DpS) algorithm to learn the hierarchical structure of social networks. Then, we devise a local search based DpS algorithm to obtain incremental information of network evolution. Finally, we show Dnps's potentials on future link prediction task for three real life large scale dynamic social networks. The results show that Dnps consistently outperforms all baseline methods and exhibits an improvement of 12% 6% 4% on Digg, Flickr and YouTube over the second highest level, respectively. Moreover, Dnps is also scalable. For example, Dnps can speed up the training process in 2 36 times compared with benchmarks on Flickr network. The source codes of the project is available online1.
IEEE INFOCOM 2017 IEEE Conference on Computer Communications