To capture the geo-social closeness between LBSN users, we propose a unified influence metric. This metric combines a novel social proximity measure named penalized hit- ting time, with a geographical weight function modeled by power law distribution.
Nov 2, 2012
In this paper, we perform an in-depth analysis of the geo-social correlations among LBSN users at event level, based on which we address two problems.
Aug 7, 2024 · In this paper, we perform an in-depth analysis of the geo-social correlations among LBSN users at event level, based on which we address two problems.
Oct 29, 2012 · In this paper, we perform an in-depth analysis of the geo-social correlations among LBSN users at event level, based on which we address two ...
In this paper, we perform an in-depth analysis of the geo-social correlations among LBSN users at event level, based on which we address two problems: user ...
The geo-social correlations among event participants make it possible to quantify mutual user influence for various events. Such a quantification of influence ...
We propose two approximate algorithms, namely global iteration (GI) and dynamic neighborhood expansion (DNE), to efficiently evaluate user influence with tight ...
Evaluating geo-social influence in location-based social networks. CIKM, 2012. CIKM 2012 · DBLP · Scholar · DOI. Full names. Links ISxN. @inproceedings{CIKM- ...
We claim to capture the interactions among virtual communities, physical mobility activities and time effects to infer the social influence between user pairs.
First, we use geographic influence for candidate selection. Furthermore, we propose a unified POI recommendation framework, which fuses user preference to a ...