TY - GEN
T1 - Positive-unlabeled learning in streaming networks
AU - Chang, Shiyu
AU - Zhang, Yang
AU - Tang, Jiliang
AU - Yin, Dawei
AU - Chang, Yi
AU - Hasegawa-Johnson, Mark A.
AU - Huang, Thomas S.
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/8/13
Y1 - 2016/8/13
N2 - Data of many problems in real-world systems such as link prediction and one-class recommendation share common characteristics. First, data are in the form of positive-unlabeled (PU) measurements (e.g. Twitter "following", Facebook "like", etc.) that do not provide negative information, which can be naturally represented as networks. Second, in the era of big data, such data are generated temporally-ordered, continuously and rapidly, which determines its streaming nature. These common characteristics allow us to unify many problems into a novel framework - PU learning in streaming networks. In this paper, a principled probabilistic approach SPU is proposed to leverage the characteristics of the streaming PU inputs. In particular, Spu captures temporal dynamics and provides real-time adaptations and predictions by identifying the potential negative signals concealed in unlabeled data. Our empirical results on various real-world datasets demonstrate the effectiveness of the proposed framework over other state-of-the-art methods in bothlink prediction and recommendation.
AB - Data of many problems in real-world systems such as link prediction and one-class recommendation share common characteristics. First, data are in the form of positive-unlabeled (PU) measurements (e.g. Twitter "following", Facebook "like", etc.) that do not provide negative information, which can be naturally represented as networks. Second, in the era of big data, such data are generated temporally-ordered, continuously and rapidly, which determines its streaming nature. These common characteristics allow us to unify many problems into a novel framework - PU learning in streaming networks. In this paper, a principled probabilistic approach SPU is proposed to leverage the characteristics of the streaming PU inputs. In particular, Spu captures temporal dynamics and provides real-time adaptations and predictions by identifying the potential negative signals concealed in unlabeled data. Our empirical results on various real-world datasets demonstrate the effectiveness of the proposed framework over other state-of-the-art methods in bothlink prediction and recommendation.
KW - Continuous time
KW - Dynamic network
KW - Online learning
KW - PU learning
KW - Streaming link prediction
KW - Streaming recommendation
UR - http://www.scopus.com/inward/record.url?scp=84985040983&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84985040983&partnerID=8YFLogxK
U2 - 10.1145/2939672.2939744
DO - 10.1145/2939672.2939744
M3 - Conference contribution
AN - SCOPUS:84985040983
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 755
EP - 764
BT - KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
Y2 - 13 August 2016 through 17 August 2016
ER -