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Found 9 papers, 1 papers with code
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs
We propose a novel Invariant Neighborhood Pattern Learning (INPL) to alleviate the distribution shifts problem on non-homophilous graphs.
Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception
The model reproduces a larger number of known empirical phenomena than previous models, in particular: (1) the effect of the time to arrival of an approaching vehicle on whether the pedestrian accepts the gap, the effect of the vehicle's speed on both (2) gap acceptance and (3) pedestrian timing of crossing in front of yielding vehicles, and (4) the effect on this crossing timing of the stopping distance of the yielding vehicle.
Modeling human road crossing decisions as reward maximization with visual perception limitations
Interestingly, our model's decisions are sensitive to not only the time gap, but also the speed of the approaching vehicle, something which has been described as a "bias" in human gap acceptance behavior.
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.
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.
