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Message passing neural network (MPNN) has recently emerged as a successful framework by achieving state-of-the-art performances on many graph-based learning tasks.
MPNN has also recently been extended to multi-relational graphs (each edge is labelled), and hypergraphs (each edge can connect any number of vertices).
However, in real-world datasets involving text and knowledge, relationships are much more complex in which hyperedges can be multi-relational, recursive, and ordered.
Such structures present several unique challenges because it is not clear how to adapt MPNN to variable-sized hyperedges in them.
In this work, we first unify exisiting MPNNs on different structures into G-MPNN (Generalised MPNN) framework.
Motivated by real-world datasets, we then propose a novel extension of the framework, MPNN-R (MPNN-Recursive) to handle recursively-structured data.
Experimental results demonstrate the effectiveness of proposed G-MPNN and MPNN-R.
Author Information
Naganand Yadati (Indian Institute of Science)
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2019 Poster: HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs »
Naganand Yadati · Madhav Nimishakavi · Prateek Yadav · Vikram Nitin · Anand Louis · Partha Talukdar