7 citations · 7 across the 2 of their papers we have counts for
3 papers
cs.DB2022
TED: Towards Discovering Top-k Edge-Diversified Patterns in a Graph Database
Kai Huang, Haibo Hu, Qingqing Ye +3
With an exponentially growing number of graphs from disparate repositories, there is a strong need to analyze a graph database containing an extensive collection of small- or mediu…
cs.IR2017★ 7 cited
A Context-Aware User-Item Representation Learning for Item Recommendation
Libing Wu, Cong Quan, Chenliang Li +2
Both reviews and user-item interactions (i.e., rating scores) have been widely adopted for user rating prediction. However, these existing techniques mainly extract the latent repr…
cs.DB2017
ProbeSim: Scalable Single-Source and Top-k SimRank Computations on Dynamic Graphs
Yu Liu, Bolong Zheng, Xiaodong He +4
Single-source and top- SimRank queries are two important types of similarity search in graphs with numerous applications in web mining, social network analysis, spam detection,…