G
Gao Cong
Researcher at Nanyang Technological University
Publications - 237
Citations - 14241
Gao Cong is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 57, co-authored 218 publications receiving 11650 citations. Previous affiliations of Gao Cong include Microsoft & Aalborg University.
Papers
More filters
Posted Content
Distributed Publish/Subscribe Query Processing on the Spatio-Textual Data Stream
TL;DR: PS2Stream as mentioned in this paper proposes a distributed publish/subscribe system, called PS2Stream, which digests a massive spatio-textual data stream and directs the stream to target users with registered interests.
Proceedings Article
DynaDiffuse: a dynamic diffusion model for continuous time constrained influence maximization
TL;DR: Although the problem is NP-hard, the influence spread functions are monotonic and submodular, enabling fast approximations on top of an innovative stochastic model checking approach, and the model finds higher quality solutions and the algorithm outperforms state-of-art alternatives.
Journal ArticleDOI
Maximizing influence under influence loss constraint in social networks
TL;DR: An alternative influence maximization problem which is naturally motivated by the reliability constraint of nodes in social networks is studied, aiming to find top-K influential nodes given a threshold of influence loss due to the failure of a subset of R nodes.
Proceedings ArticleDOI
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation
Peizhi Wu,Gao Cong +1 more
TL;DR: In this paper, a unified deep autoregressive model, UAE, is proposed to learn the joint data distribution from both the data and query workload in a single model, which achieves single-digit multiplicative error at tail and better accuracies over state-of-the-art methods.
Proceedings ArticleDOI
On Spatial Pattern Matching
TL;DR: This paper proves that answering SPM queries is computationally intractable, and proposes two efficient algorithms for their evaluation that are highly effective and efficient.