Graph anomaly detection (GAD) on text-attributed graphs (TAGs) is vital for applications such as fraud detection and academic integrity verification. Existing approaches generally fall into two paradigms. GNN-based methods capture structural patterns but struggle to capture fine-grained textual semantics. Methods integrating LLMs with graphs improve semantic understanding yet fail to fully comprehend topological relationships among neighbors. Moreover, both paradigms overlook the correspondence between textual semantics and graph topological relationships, limiting their ability to identify nodes whose semantics are inconsistent with their neighborhoods. In this paper, we formalize TAG anomaly detection as a node-to-neighborhood semantic consistency problem, where anomalies may arise from either textual semantic mismatch or topological deviation between a node and its neighbors. We propose N2NSC (Node-to-Neighborhood Semantic Consistency), a framework that captures the correspondence between graph topology and textual semantics through two complementary fusion paths. The two pathways work synergistically, enabling the LLM to jointly use textual and structural neighborhood information for anomaly detection. Extensive experiments across eight benchmark datasets, with additional validation on two real-world fraud datasets, demonstrate the effectiveness of N2NSC.

With the advent of large language models, machine translation (MT) has become a widely used, but little understood, tool for accessing historical and multilingual texts. While models like GPT, Claude, and Deepseek increasingly enable translation of low-resource and ancient languages, critical questions remain about their evaluation, optimal model selection, and the value of domain-specific training and retrieval-augmented generation setups.While AI models like GPT, Claude, and Deepseek are improving translation capabilities for low-resource and ancient languages, researchers still face important questions about how to evaluate their performance, which models work best, and whether specialized training approaches provide meaningful improvements in translation quality.This study introduces a comprehensive evaluation dataset for Buddhist Chinese to English translation, comprising 2,662 bilingual data points from 32 texts that have been selected to represent the full breadth of the Chinese Buddhist canon.We evaluate various computational metrics of translation quality (BLEU, chrF, BLEURT, GEMBA) against expert annotations from five domain specialists who rated 182 machine-generated translations. Our analysis reveals that LLM-based GEMBA scoring shows the strongest correlation with human judgment, significantly outperforming traditional metrics. We then benchmark commercial models (GPT-4 Turbo, Claude 3.5, Gemini), open-source models (Gemma 2, Deepseek-r1), and a domain-specialized model (Gemma 2 Mitra) using GEMBA. Our results demonstrate that domain-specific training enables open-weights models to achieve competitive performance with commercial systems, while also showing that retrieval-augmented generation (RAG) significantly improves translation quality for the best performing commercial models.