Hoprank Fewshot Node Class Eval

hoprank-fewshot-node-class-eval

HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification — Wang et al. (2026) (arXiv:2604.17271, 2026)

What this evaluates

Evaluates few-shot node classification on text-attributed graphs using self-supervised preference tuning. It probes the model's ability to leverage graph topology and anchor labels at inference without any supervised training, measuring both classification accuracy and inference efficiency.

Datasets

• Cora, Citeseer, Pubmed — total ?; splits: test (-1)

Metrics

• Accuracy (%) (primary) — range: percent
  • Standard classification accuracy: the fraction of correctly predicted node labels out of the total number of nodes in the evaluation set.

Input / output format

Input: Text-attributed graph nodes. At inference, K labeled nodes per class are provided as anchors. The model receives node text features and graph topology.

Output: Predicted class label for each node.

Scoring recipe

def compute_accuracy(predictions, gold_labels):
    correct = sum(1 for p, g in zip(predictions, gold_labels) if p == g)
    return (correct / len(gold_labels)) * 100

Common pitfalls

• Using labeled nodes during the self-supervised training phase; HopRank strictly uses labels only at inference as anchors.
• Reporting single-run results without averaging over 5 different random seeds, as the protocol requires standard deviations.
• Ignoring the adaptive early-exit strategy when evaluating inference efficiency, which significantly reduces compute cost.

Evidence (verbatim from paper)

Table 4.1 summarizes the overall comparison. HopRank achieves the best accuracy across all few-shot settings on Citeseer and Pubmed, and remains competitive on Cora, despite using zero labeled nodes during training. All results are averaged over 5 runs with different random seeds; standard deviations are reported as subscripts.

Citation

@misc{wang2026hoprank,
  title={HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification},
  author={Wang et al. (2026)},
  year={2026},
  note={arXiv:2604.17271}
}

• arXiv: 2604.17271