# Paper Reading: Tensor Graph Convolutional Networks for Text Classification

The basic notations for GCN are the same with this post. 1. Graph Tensor Definition Here we first describe the formal definition of graph tensor which consists of a series of graphs. is a graph tensor, where and , if and (when ). Where is the i-th graph in the graph tensor, is the set of i-th graph nodes, is the set of the i-th … Continue reading Paper Reading: Tensor Graph Convolutional Networks for Text Classification

# Paper Reading: Modeling Relational Data with Graph Convolutional Networks

This paper introduce Relational Graph Convolutional Networks (R-GCNs) which deal with relational data. In addition, techniques for parameter sharing and to enforce sparsity constraints are introduced to apply R-GCNs to multigraphs with large numbers of relations. 1. Relational Graph Convolutional Networks Given directed and labeled multi-graphs as with nodes and labeled edges , where is a relation type. In a relational multi-graph, R-GCNs calculates the … Continue reading Paper Reading: Modeling Relational Data with Graph Convolutional Networks