Large language models (LLMs) have demonstrated strong performance on coding and reasoning benchmarks; however, their ability to solve graph-structured machine learning problems remains largely unexplored. In particular, no benchmark currently evaluates whether LLMs can autonomously solve end-to-end Graph Neural Network (GNN) coding tasks under realistic competition settings. To address this gap, this paper introduces GNN-CB, the first competition-based benchmark for evaluating both humans and LLMs on GNN coding tasks. GNN-CB consists of 18 curated competitions spanning node-, edge-, and graph-level prediction across diverse graph categories, domains, and difficulty tiers. All submissions are evaluated through a unified automated pipeline with hidden test sets and standardized scoring. Human participants solve tasks under controlled competition constraints, while LLMs are evaluated using a frozen zero-shot prompting protocol based on a plan-then-code paradigm with bounded execute-and-repair loops. The benchmark additionally supports both non-agent and autonomous agent-based evaluation within the same protocol. Under our evaluated protocol, LLMs rarely match Human Top performance and show less stable performance across competitions. No single model dominates: a few competitions are won by LLMs, yet humans still hold the top score on most tasks. We release GNN-CB as a living benchmark with automated evaluation infrastructure, dynamic leaderboards, and reproducible execution pipelines. Beyond benchmarking, GNN-CB provides a practice-oriented resource for studying GNN implementation across progressively diverse graph-learning tasks. The benchmark and evaluation framework are publicly available at https://basiralab.github.io/GNN-CB/.

Extracting entities and relations from scientific text is challenging due to long sentences with densely packed entities. Pipeline approaches address this by first extracting entities and then predicting relations between all possible entity pairs. Since the relation extraction phase operates over this exhaustive set, the inclusion of candidate pairs that may be semantically related but lack syntactic proximity introduces precision errors, ultimately reducing Rel+ F1 metric. We propose a simple yet effective syntactic filtering method based on dependency parsing to prune unlikely entity pairs before relation prediction. By leveraging syntactic proximity in the dependency parse tree, our approach retains structurally plausible pairs and reduces false positives in downstream relation classification. Our method is grounded in consistent statistical patterns observed across all evaluated datasets, reinforcing its generalizability and effectiveness. We integrate this filtering step into architectures such as PL-Marker and HGERE, and evaluate its impact across multiple datasets. Our method improves Rel+ F1 scores significantly by an absolute increase of 3.5–10.3% on SciERC, SciER, and ACE05 datasets. These results highlight the importance of syntactic cues for accurate relation extraction in complex domains like scientific literature.