Why I'm lukewarm on graph neural networks (singlelunch.com)

Despite the hype around Graph Neural Networks (GNNs) as cutting-edge tools for graph-structured data, this analysis expresses caution, arguing that the field may be overfocused on incremental GNN variants while simpler, first-order embedding methods often suffice. The author highlights that graphs fundamentally boil down to adjacency matrices—essentially numeric matrices that can be factorized or compressed, much like methods used in NLP such as Word2Vec or GloVe. These simpler approaches often perform comparably on tasks like node classification and clustering, while higher-order methods or deep GNN architectures provide clear advantages primarily in more complex tasks like link prediction. The critique extends to the research culture itself, where academic incentives encourage minor tweaks and convoluted algorithmic changes that yield marginal improvements on small, outdated datasets rather than driving fundamental progress. Particularly problematic is the lack of rigorous baseline comparisons and poor code efficiency, which hampers reproducibility and practical adoption. The author advocates for focusing on algorithmic efficiency, proper benchmarking on large real-world datasets, and understanding when more complex methods actually bring value. In sum, the piece urges the AI/ML community to temper enthusiasm for flashy GNN variants and instead prioritize scalable, interpretable embeddings and practical experimentation frameworks to foster meaningful advances in graph learning.