Graph Neural Networks (GNNs) are attracting growing attention due to their effectiveness and flexibility in modeling a variety of graph-structured data. Exiting GNN architectures usually adopt simple pooling operations (eg. sum, average, max) when aggregating messages from a local neighborhood for updating node representation or pooling node representations from the entire graph to compute the graph representation. Though simple and effective, these linear operations do not model high-order non-linear interactions among nodes. We propose the Tensorized Graph Neural Network (tGNN), a highly expressive GNN architecture relying on tensor decomposition to model high-order non-linear node interactions. tGNN leverages the symmetric CP decomposition to efficiently parameterize permutation-invariant multilinear maps for modeling node interactions. Theoretical and empirical analysis on both node and graph classification tasks show the superiority of tGNN over competitive baselines. In particular, tGNN achieves the most solid results on two OGB node classification datasets and one OGB graph classification dataset.

翻译：图形神经网络(GNNs)因其在模拟各种图形结构数据方面的有效性和灵活性而正在引起越来越多的关注。退出GNN结构的架构通常采用简单的集合操作(例如,总和,平均,最大),当从整个图表中汇总来自当地社区的信息以更新节点代表或集合节点代表,以计算图形代表时,这些线性操作虽然简单而有效,但并不模拟各节点之间的高阶非线性互动。我们提议Tensorized图形神经网络(tGNN),这是一个高度直观的GNN结构,依赖高压分解,以模拟高阶非线性节点互动。tGNN利用对称式的CP分解定位,以高效参数化调异性多线性多线性地图,以模拟节点互动。关于节点和图形分类任务的理论和实验分析显示tGNNe优于竞争性基线。特别是,TGNNN在两个OGB节点分类数据集和一个OGB图形分类数据集上取得了最坚实的结果。