Does Future Link Prediction on Temporal Text-Attributed Graphs Need Heavyweight Modeling?

Future link prediction on Temporal Text-Attributed Graphs (TTAGs) increasingly relies on heavyweight models that process fine-grained interaction histories with temporal Transformers or large language models (LLMs). This modeling complexity incurs substantial computational cost, yet it remains unclear how much is necessary. We explore this question empirically and find that our lightweight Snapshot GNN built on reusable temporal snapshots and inexpensive global heuristics remains surprisingly competitive with substantially heavier models, while being much more efficient. To explain this result, we derive a theoretical bound on the optimal AUC advantage of the full temporal history over a compressed additive summary. Guided by the analysis, we perform controlled experiments to separate historical evidence from processing machinery and observe limited returns from both longer, finer-grained histories and heavier graph processing. LLMs still retain an advantage given comparable historical information, and graph models require additional structural cues to close the gap. This motivates us to examine where the remaining LLM advantage arises. We discover that GNN uncertainty alone can localize the predictions on which the LLM is particularly beneficial, which tend to correlate with weak structural support from the graph. When used for selective LLM invocation, these localized gains are substantially larger than their aggregate effect on global performance, while uncertainty remains highly competitive with learned localizers. Overall, our results suggest that future link prediction on TTAGs can often achieve strong accuracy at considerably lower computational cost across real-world benchmarks, with LLMs providing localized gain where graph models are least certain. Our code is available at https://anonymous.4open.science/r/dyg-6686