Embodiments of the present disclosure provide a Space-Time Factor Graph Model (STFGM) incorporating time and space correlations to infer the authors' missing high-resolution affiliations with time in academic social network. What's more, at a personal global level, devising different smoothing methods to bridge the "holes" between years and trim the "glitches" according to different priority goals of increasing information items with the least precision loss or increasing precision with the least information items trimmed, and demonstrating that our STFGM outperforms the baselines 6%-27% in two datasets (Aminer and MAG) and on two precision metrics. Further, the devised smoothing methods can gain 5%-18% items growth with only a minor precision loss about 0.05%-1% or achieve 2%-7% precision increasing with 3%-7% items loss according to different priority settings. At last, two applications are developed based on our inferring model and smoothing method which demonstrate the effectiveness further.
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