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Found 9 papers, 3 papers with code
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling
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.
MTHetGNN: A Heterogeneous Graph Embedding Framework for Multivariate Time Series Forecasting
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.
Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting
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.
TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding
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.
Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation
Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector.
CoSimGNN: Towards Large-scale Graph Similarity Computation
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.
Multivariate Time Series Forecasting with Transfer Entropy Graph
Multivariate time series (MTS) forecasting is an essential problem in many fields.
Improved object recognition using neural networks trained to mimic the brain's statistical properties
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.
