UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node
  Classification
Minhao Zou Zhongxue Gan Yutong Wang Junheng Zhang Dongyan Sui Chun Guan Siyang Leng
Graph and hypergraph representation learning has attracted increasing
attention from various research fields. Despite the decent performance and
fruitful applications of Graph Neural Networks (GNNs), Hypergraph Neural
Networks (HGNNs), and their well-designed variants, on some commonly used
benchmark graphs and hypergraphs, they are outperformed by even a simple
Multi-Layer Perceptron. This observation motivates a reexamination of the
design paradigm of the current GNNs and HGNNs and poses challenges of
extracting graph features effectively. In this work, a universal feature
encoder for both graph and hypergraph representation learning is designed,
called UniG-Encoder. The architecture starts with a forward transformation of
the topological relationships of connected nodes into edge or hyperedge
features via a normalized projection matrix. The resulting edge/hyperedge
features, together with the original node features, are fed into a neural
network. The encoded node embeddings are then derived from the reversed
transformation, described by the transpose of the projection matrix, of the
network's output, which can be further used for tasks such as node
classification. The proposed architecture, in contrast to the traditional
spectral-based and/or message passing approaches, simultaneously and
comprehensively exploits the node features and graph/hypergraph topologies in
an efficient and unified manner, covering both heterophilic and homophilic
graphs. The designed projection matrix, encoding the graph features, is
intuitive and interpretable. Extensive experiments are conducted and
demonstrate the superior performance of the proposed framework on twelve
representative hypergraph datasets and six real-world graph datasets, compared
to the state-of-the-art methods. Our implementation is available online at
https://github.com/MinhZou/UniG-Encoder.