  We study the problem of floor identification for radiofrequency (RF) signal
samples obtained in a crowdsourced manner, where the signal samples are highly
heterogeneous and most samples lack their floor labels. We propose GRAFICS, a
graph embedding-based floor identification system. GRAFICS first builds a
highly versatile bipartite graph model, having APs on one side and signal
samples on the other. GRAFICS then learns the low-dimensional embeddings of
signal samples via a novel graph embedding algorithm named E-LINE. GRAFICS
finally clusters the node embeddings along with the embeddings of a few labeled
samples through a proximity-based hierarchical clustering, which eases the
floor identification of every new sample. We validate the effectiveness of
GRAFICS based on two large-scale datasets that contain RF signal records from
204 buildings in Hangzhou, China, and five buildings in Hong Kong. Our
experiment results show that GRAFICS achieves highly accurate prediction
performance with only a few labeled samples (96% in both micro- and macro-F
scores) and significantly outperforms several state-of-the-art algorithms (by
about 45% improvement in micro-F score and 53% in macro-F score).
