Multi-modal entity alignment aims to identify equivalent entities across different multi-modal knowledge graphs (MMKGs). While prior work has achieved notable progress through improved multi-modal encoding and cross-modal fusion techniques, two critical challenges remain unresolved. First, due to the heterogeneous and often inconsistent sources from which MMKGs are constructed, the quality and informativeness of modalities vary significantly across entities, leading to the modality weighting problem. Second, existing cross-modal fusion mechanisms predominantly emphasize modality-shared information, often at the expense of modality-specific signals that are also essential for precise alignment. To address these issues, we propose HUMEA, a novel framework that integrates hierarchical Mixture-of-Experts (MoE) with unimodal distillation. HUMEA consists of: (1) A hierarchical MoE module comprising intra-modal and inter-modal experts, which adaptively modulates modality contributions by capturing entity representations at fine-to-coarse semantic granularities. In addition, we introduce a contrastive mutual information loss to enhance expert diversity and reduce redundancy. (2) A unimodal distillation strategy that preserves modality-specific information in the fused representations through single-modality alignment and distillation, achieving a balanced integration of shared and unique modality features. Extensive experiments on two benchmark datasets, FB15K-DB15K and FB15K-YAGO15K, demonstrate state-of-the-art performance, validating the effectiveness of our approach.

Large Language Models (LLMs) have shown immense potential in Knowledge Graph Completion (KGC), yet bridging the modality gap between continuous graph embeddings and discrete LLM tokens remains a critical challenge.While recent quantization-based approaches attempt to align these modalities, they typically treat quantization as flat numerical compression, resulting in semantically entangled codes that fail to mirror the hierarchical nature of human reasoning.In this paper, we propose GS-Quant, a novel framework that generates semantically coherent and structurally stratified discrete codes for KG entities.Unlike prior methods, GS-Quant is grounded in the insight that entity representations should follow a linguistic coarse-to-fine logic.We introduce a Granular Semantic Enhancement module that injects hierarchical knowledge into the codebook, ensuring that earlier codes capture global semantic categories while later codes refine specific attributes.Furthermore, a Generative Structural Reconstruction module imposes causal dependencies on the code sequence, transforming independent discrete units into structured semantic descriptors.By expanding the LLM vocabulary with these learned codes, we enable the model to reason over graph structures isomorphically to natural language generation.Experimental results demonstrate that GS-Quant significantly outperforms existing text-based and embedding-based baselines.Our code is publicly available at https: //github.com/mikumifa/GS-Quant.

Graph anomaly detection (GAD) aims to identify nodes that deviate from normal patterns in structure or features. While recent GNN-based approaches have advanced this task, they struggle with two major challenges: 1) homophily disparity, where nodes exhibit varying homophily at both class and node levels; and 2) limited scalability, as many methods rely on costly whole-graph operations. To address them, we propose SAGAD, a Scalable and Adaptive framework for GAD. SAGAD precomputes multi-hop embeddings and applies reparameterized Chebyshev filters to extract low- and high-frequency information, enabling efficient training and capturing both homophilic and heterophilic patterns. To mitigate node-level homophily disparity, we introduce an Anomaly Context-Aware Adaptive Fusion, which adaptively fuses low- and high-pass embeddings using fusion coefficients conditioned on Rayleigh Quotient-guided anomalous subgraph structures for each node. To alleviate class-level disparity, we design a Frequency Preference Guidance Loss, which encourages anomalies to preserve more high-frequency information than normal nodes. SAGAD supports mini-batch training, achieves linear time and space complexity, and drastically reduces memory usage on large-scale graphs. Theoretically, SAGAD ensures asymptotic linear separability between normal and abnormal nodes under mild conditions. Extensive experiments on 10 benchmarks confirm SAGAD's superior accuracy and scalability over state-of-the-art methods.

Multi-modal entity alignment (MMEA) aims to identify equivalent entities across heterogeneous multi-modal knowledge graphs (MMKGs), which play a crucial role in organizing and integrating web knowledge from diverse modalities. Although prior studies have made progress by multi-modal features fusion, three inherent limitations remain unresolved. First, instance-level feature fusion is misaligned with the pair-wise task format of MMEA. Second, joint representations often overlook modality-specific characteristics, resulting in insufficient alignment. Third, most existing methods rely solely on global features of modality. This may lead to the misalignment of entities that are similar yet distinct. To address above issues, we propose DMEA, a new decision-fusion-based framework. Specifically, we first design a multi-modal knowledge encoding module to extract both global and local features for different modalities and then introduce a multi-grained alignment module, which consists of two components: intra-modal alignment and cross-modal alignment. The former computes alignment scores between the global and local features of entity pairs within the same modality, while the latter leverages the complementarity across modalities to compute cross-modal alignment scores. Each score is regarded as an independent decision, and the final alignment judgment is made by integrating all decisions. Finally, we incorporate an intra-modal contrastive loss to obtain more discriminative embedding representations. DMEA achieves improvements of 13.2% and 14.8% in hit@1 over the state-of-the-art models on two benchmark datasets, FB15K-DB15K and FB15K-YAGO15K, respectively, validating the superiority of our framework.