Jul 2026· 2026 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)· pp. 563-570· 0 citations· 21 references
Abstract
Building on GraphLawPH, the first knowledge graph of Philippine Republic Acts, this study benchmarks three graph neural network (GNN) architectures—GAT, GCN, and GraphSAGE—and compares them to a high-dimensional Legal-BERT baseline to determine their suitability for statutory legal embedding. Using a corpus of 12,124 Republic Acts linked by 3,721 citation edges, the researchers evaluate the models as high-fidelity semantic compressors that reduce dimensionality by 91.7% while maintaining extreme representational alignment. Performance was rigorously assessed on semantic similarity detection and community discovery using retrieval metrics and structural alignment scores. The results reveal a 3.1% graph-gain for GAT in semantic retrieval (MRR=0.7870) and a massive 2,680% modularity improvement for GCN (0.4227) over the text-only baseline (0.0152), meeting established benchmarks for substantial community structure. These findings resolve the Sparsity Paradox for the corpus’s 81.79% isolated nodes, proving that transferable inductive biases from the connected minority effectively preserve semantic integrity across the entire legal graph. This study provides the first empirical benchmark for Philippine statutory GNNs, delivering guidance for selecting architectures that balance local semantic precision with global structural coherence.
Assessing scientific novelty is inherently complex, requiring evaluation of both a paper's internal structure and its contribution within the broader research landscape. Existing large language model (LLM) approaches often rely on surface-level similarity or citation retrieval, overlooking the integration between a paper's content and its contextual grounding in related literature. To address this gap, we introduce SciNova, a benchmark containing 3,063 papers from both ICLR and NeurIPS, with full content, bibliographies, and peer review scores for novelty prediction. Building on this benchmark, we propose GraphMind, a model that jointly processes micro- and macro-level structures for novelty prediction and rationale generation. It represents each paper as a hierarchical graph that captures its claims, methods, and experiments (micro-level), while its related paper graphs encode both cited and semantically similar works (macro-level). Experiments show that by incorporating this bi-level graph, GraphMind significantly outperforms baseline LLMs in both novelty score prediction and rationale generation.
Italo Luis da Silva, Hanqi Yan, Lin Gui et al.· Proceedings of the 32nd ACM...· 1 citation
Graph neural networks (GNNs) are typically conceptualized as message-passing neural networks, yet it remains unclear why neighborhood aggregation reliably outperforms node-wise multilayer perceptrons (MLPs). Despite its empirical success, this paradigm can be computationally expensive and sensitive to imperfect graph structures. In this work, we present a retrieval-augmented view of GNNs: each layer makes predictions by applying an MLP to a node representation together with a permutation-invariant summary of retrieved graph context. Motivated by this perspective, we propose RTA, a simple MLP-based framework that replaces structural message passing with label-aware retrieval and propagation. We provide theoretical insights that (i) connect retrieval-based aggregation to softmax-attention message passing, and (ii) establish the robustness of retrieved-context supervision to mis-retrieved outliers. Experiments on multiple text-attributed graph benchmarks show that RTA matches or even outperforms strong GNN and graph LLM baselines while improving efficiency and robustness across diverse scenarios.
Jintang Li, Yuhong Chen, Ruo-Fan Wu et al.· 0 citations
Similarity-guided Structural Matching Learning for Graph Dataset Condensation (SSGDC) is proposed, which efficiently reduces repository size while maintaining both task performance and structural information.
Yiyang Zhang, Yutong Ye, Yingbo Zhou et al.· 0 citations
Experiments show that replacing PageRank with alternative centralities yields similar F1-scores while offering notable runtime savings, and that GraphHD-Order remains competitive with the original GraphHD baseline while providing consistent speedups in encoding time.
Financial regulatory documents are characterized by their fine-grained complexity and pronounced heterogeneity, featuring specialized domain-specific content and diverse structural formats that vary across different regulatory frameworks and jurisdictions. These characteristics challenge modern Question-Answering (QA) systems, which often suffer from limited domain adaptation, poor interpretability, and hallucinatory problems. This work was conducted within a banking and software company, where such challenges directly impact regulatory compliance efforts. Our goal is to introduce RegulQA, a hybrid QA system that can extract accurate, logical, and comprehensible answers from unstructured regulatory documents. RegulQA integrates knowledge graph reasoning, semantic search, and retrieval-augmented generation using large language models. Experimental evaluation shows that RegulQA improves QA performance and significantly reduces hallucination rates. The baseline model employing only the LLM demonstrated a hallucination rate of 24%, whereas the proposed approach, combining knowledge graph reasoning and semantic retrieval with LLM reasoning, effectively reduced the hallucination rate to approximately the half. This integrated approach also yielded the best balanced overall scores across key qualitative attributes, including coverage, non-redundancy, readability, and response quality.
Amal Ben Abdelhafidh, Montassar Ben Messaoud, M. Tounsi et al.· Annual International Compute...· 0 citations