Description:
Currently, graph-level embeddings are obtained using simple sum/mean pooling over node embeddings. While effective, this approach may not capture more complex structural information. We should explore alternative graph pooling methods.
Two main alternatives:
-
Set-based pooling with LSTMs + Attention:
Inspired by Vinyals et al. (2015), this approach iteratively applies attention-based aggregation guided by an LSTM. This allows the pooling process to selectively weight node embeddings and capture richer dependencies.
-
Graph Coarsening / Clustering approaches:
Instead of treating pooling as a pure set problem, graph topology can be exploited by clustering or coarsening nodes into groups before pooling. This preserves structural information and provides more meaningful graph-level embeddings.
References:
- Section 5.5 Graph Pooling of the Book Graph representation learning / William L. Hamilton, McGill University
Description:
Currently, graph-level embeddings are obtained using simple sum/mean pooling over node embeddings. While effective, this approach may not capture more complex structural information. We should explore alternative graph pooling methods.
Two main alternatives:
Set-based pooling with LSTMs + Attention:
Inspired by Vinyals et al. (2015), this approach iteratively applies attention-based aggregation guided by an LSTM. This allows the pooling process to selectively weight node embeddings and capture richer dependencies.
Graph Coarsening / Clustering approaches:
Instead of treating pooling as a pure set problem, graph topology can be exploited by clustering or coarsening nodes into groups before pooling. This preserves structural information and provides more meaningful graph-level embeddings.
References: