Fatemeh Gholamzadeh Nasrabadi

Fatemeh Gholamzadeh Nasrabadi, AmirHossein Kashani, Pegah Zahedi et al.

In recent years, graph neural networks (GNNs) have become a popular tool for solving various problems over graphs. In these models, the link structure of the graph is typically exploited and nodes' embeddings are iteratively updated based on adjacent nodes. Nodes' contents are used solely in the form of feature vectors, served as nodes' first-layer embeddings. However, the filters or convolutions, applied during iterations/layers to these initial embeddings lead to their impact diminish and contribute insignificantly to the final embeddings. In order to address this issue, in this paper we propose augmenting nodes' embeddings by embeddings generated from their content, at higher GNN layers. More precisely, we propose models wherein a structural embedding using a GNN and a content embedding are computed for each node. These two are combined using a combination layer to form the embedding of a node at a given layer layer. We suggest methods such as using an auto-encoder or building a content graph, to generate content embeddings. In the end, by conducting experiments over several real-world datasets, we demonstrate the high accuracy and performance of our models.

Daniel Herbst, Lea Karbeska, Divyanshu Kumar et al.

While promising, graph reasoners based on Large Language Models (LLMs) lack built-in invariance to symmetries in graph representations. Operating on sequential graph serializations, LLMs can produce different outputs under node reindexing, edge reordering, or formatting changes, raising robustness concerns. We systematically analyze these effects, studying how fine-tuning impacts encoding sensitivity as well generalization on unseen tasks. We propose a principled decomposition of graph serializations into node labeling, edge encoding, and syntax, and evaluate LLM robustness to variations of each of these factors on a comprehensive benchmarking suite. We also contribute a novel set of spectral tasks to further assess generalization abilities of fine-tuned reasoners. Results show that larger (non-fine-tuned) models are more robust. Fine-tuning reduces sensitivity to node relabeling but may increase it to variations in structure and format, while it does not consistently improve performance on unseen tasks.