Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks (Extended Abstract)

Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, Aram Galstyan

Abstract: We propose to model dependence within a network view using the temporal latent space model, which uses a time-dependent low-dimensional geometric projections to represent the high-dimensional dependence structure in time-varying networks. Once we obtain the lowdimensional temporal latent space representation for graphs from time 1 to t, we can accurately predict future links in time t + 1 (i.e., Gt+1). We present a global optimization algorithm to effectively infer the temporal latent space using block coordinate gradient descent (BCGD). We further introduce two new variants of BCGD: a local BCGD algorithm and an incremental BCGD algorithm, to scale the inference algorithm to massive networks.

External IDs:dblp:conf/icde/ZhuGYSG17