no code implementations • 28 Feb 2023 • Yufan Sheng, Xin Cao, Yixiang Fang, Kaiqi Zhao, Jianzhong Qi, Gao Cong, Wenjie Zhang
In this paper, we propose WISK, a learned index for spatial keyword queries, which self-adapts for optimizing querying costs given a query workload.
no code implementations • 12 Aug 2022 • Linhao Luo, Yixiang Fang, Moli Lu, Xin Cao, Xiaofeng Zhang, Wenjie Zhang
Most of existing relevance measures focus on homogeneous networks where objects are of the same type, and a few measures are developed for heterogeneous graphs, but they often need the pre-defined meta-path.
no code implementations • NeurIPS 2021 • Yu Hao, Xin Cao, Yufan Sheng, Yixiang Fang, Wei Wang
Keyword search is a fundamental task to retrieve information that is the most relevant to the query keywords.
1 code implementation • 5 Sep 2021 • Linhao Luo, Yixiang Fang, Xin Cao, Xiaofeng Zhang, Wenjie Zhang
With the surge of graph embedding mechanism, it has also been adopted to community detection.
no code implementations • 24 Jun 2021 • Shuang Li, Lu Wang, Xinyun Chen, Yixiang Fang, Yan Song
In this paper, we model the propagation of the COVID-19 as spatio-temporal point processes and propose a generative and intensity-free model to track the spread of the disease.
no code implementations • 25 Nov 2020 • Linhao Luo, Liqi Yang, Ju Xin, Yixiang Fang, Xiaofeng Zhang, Xiaofei Yang, Kai Chen, Zhiyuan Zhang, Kai Liu
In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation.
1 code implementation • 16 Jul 2020 • Yu Hao, Xin Cao, Yixiang Fang, Xike Xie, Sibo Wang
In attributed graphs, both the structure and attribute information can be utilized for link prediction.