GC-Flow: A Graph-Based Flow Network for Effective Clustering

• URL: http://arxiv.org/abs/2305.17284v1
• Date: Fri, 26 May 2023 22:11:38 GMT
• Title: GC-Flow: A Graph-Based Flow Network for Effective Clustering
• Authors: Tianchun Wang, Farzaneh Mirzazadeh, Xiang Zhang, Jie Chen
• Abstract summary: Graph convolutional networks (GCNs) are emphdiscriminative models that directly model the class posterior $p(y|mathbfx)$ for semi-supervised classification of graph data. In this work, we design normalizing flows that replace GCN layers, leading to a emphgenerative model that models both the class conditional likelihood $p(mathbfx|y)$ and the class prior $p(y)$. The resulting neural network, GC-Flow, retains the graph convolution operations while being equipped with
• Score: 10.354035049272095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph convolutional networks (GCNs) are \emph{discriminative models} that directly model the class posterior $p(y|\mathbf{x})$ for semi-supervised classification of graph data. While being effective, as a representation learning approach, the node representations extracted from a GCN often miss useful information for effective clustering, because the objectives are different. In this work, we design normalizing flows that replace GCN layers, leading to a \emph{generative model} that models both the class conditional likelihood $p(\mathbf{x}|y)$ and the class prior $p(y)$. The resulting neural network, GC-Flow, retains the graph convolution operations while being equipped with a Gaussian mixture representation space. It enjoys two benefits: it not only maintains the predictive power of GCN, but also produces well-separated clusters, due to the structuring of the representation space. We demonstrate these benefits on a variety of benchmark data sets. Moreover, we show that additional parameterization, such as that on the adjacency matrix used for graph convolutions, yields additional improvement in clustering.

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