An Attribute Graph Embedding Algorithm for Sensing Topological and Attribute Influence

The unsupervised attribute graph embedding technique aims to learn low-dimensional node embedding using neighborhood topology and attribute information under unlabeled data. Current unsupervised models are mostly based on graph self-encoders, but full-batch training limits the scalability of the model and ignores attribute integrity when reconstructing the topology. In order to solve the above problems while considering the unsupervised learning of the model and full use of node information, this paper proposes a graph neural network architecture based on a graph self-encoder to capture the nonlinearity of the attribute graph data, and an attribute graph embedding algorithm that explicitly models the influence of neighborhood information using a multi-level attention mechanism. Specifically, the proposed algorithm fuses topology information and attribute information using a lightweight sampling strategy, constructs an unbiased graph self-encoder on the sampled graph, implements topology aggregation and attribute aggregation, respectively, models the correlation between topology embedding and attribute embedding, and considers multi-level loss terms.