no code implementations • 12 Oct 2020 • Ziheng Duan, Haoyan Xu, Yueyang Wang, Yida Huang, Anni Ren, Zhongbin Xu, Yizhou Sun, Wei Wang
Then we combine GNNs and our proposed variational graph pooling layers for joint graph representation learning and graph coarsening, after which the graph is progressively coarsened to one node.
no code implementations • 19 Aug 2020 • Yueyang Wang, Ziheng Duan, Yida Huang, Haoyan Xu, Jie Feng, Anni Ren
To characterize complex relations among variables, a relation embedding module is designed in MTHetGNN, where each variable is regarded as a graph node, and each type of edge represents a specific static or dynamic relationship.
no code implementations • 18 Aug 2020 • Yifu Zhou, Ziheng Duan, Haoyan Xu, Jie Feng, Anni Ren, Yueyang Wang, Xiaoqian Wang
In this paper, a MTS forecasting framework that can capture the long-term trends and short-term fluctuations of time series in parallel is proposed.
1 code implementation • 2 Jun 2020 • Zhiping Xiao, Weiping Song, Haoyan Xu, Zhicheng Ren, Yizhou Sun
However, the incompleteness of the labels and the features in social network datasets is tricky, not to mention the enormous data size and the heterogeneousity.
no code implementations • 16 May 2020 • Haoyan Xu, Ziheng Duan, Jie Feng, Runjian Chen, Qianru Zhang, Zhongbin Xu, Yueyang Wang
Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector.
no code implementations • 14 May 2020 • Haoyan Xu, Runjian Chen, Yueyang Wang, Ziheng Duan, Jie Feng
In this paper, we focus on similarity computation for large-scale graphs and propose the "embedding-coarsening-matching" framework CoSimGNN, which first embeds and coarsens large graphs with adaptive pooling operation and then deploys fine-grained interactions on the coarsened graphs for final similarity scores.
2 code implementations • 3 May 2020 • Ziheng Duan, Haoyan Xu, Yida Huang, Jie Feng, Yueyang Wang
Multivariate time series (MTS) forecasting is an essential problem in many fields.
1 code implementation • 25 May 2019 • Callie Federer, Haoyan Xu, Alona Fyshe, Joel Zylberberg
To test this, we trained DCNNs on a composite task, wherein networks were trained to: a) classify images of objects; while b) having intermediate representations that resemble those observed in neural recordings from monkey visual cortex.