Jan 24, 2023 · In this paper, we first design a new metric, named Neighborhood Homophily (\textit{NH}), to measure the label complexity or purity in the ...

Oct 21, 2023 · In this paper, we first design a new metric, Neighborhood Homophily (NH), to measure the label complexity or purity in node neighborhoods.

Oct 6, 2023 · In this paper, we first design a new metric, Neighborhood. Homophily (NH), to measure the label complexity or purity in node neighborhoods.

In this paper, we first design a new metric, Neighborhood Homophily (\textit{NH}), to measure the label complexity or purity in node neighborhoods. Furthermore, ...

Neighborhood Homophily-based Graph Convolutional Network Shengbo Gong Institute of Cyberspace Security Zhejiang University of Technology Hangzhou, China

We propose a novel neighborhood distribution-guided graph convolutional network, which can adaptively combine lower-order and higher-order neighborhood ...

This work proposes a novel Neighbors Selective Graph Convolutional Network (NSGCN), which allows nodes to selectively receive relevant neighbor information.

Dec 23, 2022 · We first propose a metric, namely, neighborhood class consistency (NCC), to quantitatively characterize the neighborhood patterns of graph datasets.

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When applied to semi-supervised node classification, GNNs are widely believed to work well due to the homophily assumption (“like attracts like”), and fail to ...