# 5 Graph Structure Learning in GNN

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Category: { Graph Neural Network }
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Summary: We can learn a graph structure without prior knowledge
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# 4 Mix-hop Propagation in GNN

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Category: { Graph Neural Network }
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Summary: Mix-hop is a strategy to avoid oversmoothing in GNN
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# 3 Time Convolution

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Category: { Time Series }
Summary: The temporal convolution is responsible for capturing temporal patterns in a sequence.
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# 1 Prediction Space in Forecasting

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Category: { Time Series }
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Summary: In a forecasting problem, we have $\mathcal P$, the priors, e.g., price and demand is negatively correlated, $\mathcal D$, available dataset, $Y$, the observations, and $F$, the forecasts. Information Set $\mathcal A$ The priors $\mathcal D$ and the available data $\mathcal P$ can be summarized together as the information set $\mathcal A$. Under a probabilistic view, a forecaster will find out or approximate a CDF $\mathcal F$ such that1 $$\mathcal F(Y\vert \mathcal D, \mathcal P) \to F.$$ Naively speaking, once the density $\rho(F, Y)$ is determined or estimated, a probabilistic forecaster can be formed.
Pages: 4