Entity Alignment (EA) aims to identify equivalent entities across multiple Knowledge Graphs (KGs). However, when applied to larger-scale KGs, most existing EA approaches suffer from the scalability issue due to excessive GPU memory and time consumption. To mitigate this, recent advances have introduced the Large-scale EA (LsEA) task, which divides large-scale KG pairs into smaller sub-graph pairs. Despite their promising results, several notable challenges remain, preventing these advances from achieving optimal performance: 1) How to effectively utilize critical structures when generating sub-tasks? 2) How to supplement high-quality context to enhance LsEA performance? 3) How to address scenarios without alignment seeds? To tackle these challenges, we propose a novel method called ELsEA. It comprises three main components: (1) Source and Target Graph Partition, using a Metis-based weighted partitioner and a counter-part candidate generator to partition source and target graphs respectively, aiming to utilize critical structures effectively; (2) Supplement High-quality Context, which utilizes a value-based informativeness-evaluation module and a neighbor enrichment module to assess each entity's informativeness effectively, then supplement high-quality context based on this informativeness; and (3) Seed-free Setup, introducing a mixed-info pseudo-seed generation strategy to mitigate name bias, generating accurate pseudo-seeds when alignment seeds are unavailable. Extensive experiments demonstrate that ELsEA outperforms state-of-the-art baselines. The code of ELsEA is available online11https://githuh.com/wx-qzhou/ELsEA.git.

Multi-modal recommender systems (MMRSs) have demonstrated significant potential in mitigating data sparsity and cold start problems by leveraging diverse multi-modal data, such as text and images. To further improve the recommendation accuracy, some MMRSs have integrated knowledge graphs (KGs) to enrich the graph structure with meaningful relationships between entities, giving rise to the task of KG-based MMRSs. Despite the promising results achieved by existing studies on this task, (i) they overlook the substantial noise introduced within the auxiliary information (i.e., both KGs and multi-modal data), and (ii) most of them struggle to effectively align the knowledge from history user-item interactions and auxiliary information. To tackle these limitations, we propose a novel model entitled MAGIC (noise Mitigation and knowledge Aignment for knowledge Graph-based multI-modal reCommendation). Specifically, to tackle the limitation (i), we design a noise-aware heterogeneous aggregation layer in the KG-based modal enhancement module. To address the limitation (ii), MAGIC introduces adversarial learning in the CF-based adversarial learning module, and exploits contrastive learning in the fusion and prediction module. The experiments conducted on two extended real-world datasets from different domains demonstrate the superiority of MAGIC over state-of-the-art baselines.

Multi-Modal Knowledge Graphs (MMKGs), comprising relational triples and related multi-modal data (e.g., text and images), usually suffer from the problems of low coverage and incompleteness. To mitigate this, existing studies introduce a fundamental MMKG fusion task, i.e., Multi-Modal Entity Alignment (MMEA) that identifies equivalent entities across multiple MMKGs. Despite MMEA's significant advancements, effectively integrating MMKGs remains challenging, mainly stemming from two core limitations: 1) entity ambiguity, where real-world entities across different MMKGs may possess multiple corresponding counterparts or alternative identities; and 2) severe noise within multi-modal data. To tackle these limitations, a new task MMER (Multi-Modal Entity Resolution), which expands the scope of MMEA to encompass entity ambiguity, is introduced. To tackle this task effectively, we develop a novel model ADMH-ER (Adaptive Denoising Multi-modal Hybrid for Entity Resolution) that incorporates several crucial modules: 1) multi-modal knowledge encoders, which are crafted to obtain entity representations based on multi-modal data sources; 2) an adaptive denoising multi-modal hybrid module that is designed to tackle challenges including noise interference, multi-modal heterogeneity, and semantic irrelevance across modalities; and 3) a hierarchical multi-objective learning strategy, which is proposed to ensure diverse convergence capabilities among different learning objectives. Experimental results demonstrate that ADMH-ER outperforms state-of-the-art methods.

Few-shot learning for image classification task aims to classify images from several novel classes with limited number of samples. Recent studies have shown that the deep local descriptors have better representation ability than image-level features, and achieve great success. However, most of these methods often use all local descriptors or over-screening local descriptors for classification. The former contains some task-irrelevant descriptors, which may misguide the final classification result. The latter is likely to lose some key descriptors. In this paper, we propose a novel Task-Aware Discriminant local descriptors Network (TADNet) to address these issues, which can adaptively select the discriminative query descriptors and eliminate the task-irrelevant query descriptors among the entire task. Specifically, TADNet assigns a value to each query descriptor by comparing its similarity to all support classes to represent its discriminant power for classification. Then the discriminative query descriptors can be preserved via a task-aware attention map. Extensive experiments on both fine-grained and generalized datasets demonstrate that the proposed TADNet outperforms the existing state-of-the-art methods.

In this paper, we technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for clustering deep features. To improve the representation and clustering abilities, DSCF-Net explicitly considers discovering hidden deep semantic features, enhancing the robustness properties of the deep factorization to noise and preserving the local manifold structures of deep features. Specifically, DSCF-Net integrates the robust deep concept factorization, deep self-expressive representation and adaptive locality preserving feature learning into a unified framework. To discover hidden deep representations, DSCF-Net designs a hierarchical factorization architecture using multiple layers of linear transformations, where the hierarchical representation is performed by formulating the problem as optimizing the basis concepts in each layer to improve the representation indirectly. DSCF-Net also improves robustness by subspace recovery for sparse error correction firstly and then performs deep factorization in the recovered visual subspace. To obtain localitypreserving representations, we also present an adaptive deep self-representative weighting strategy by using the coefficient matrix as adaptive weights to keep the locality of representations. Extensive results show that DSCF-Net delivers state-of-the-art performance on several public databases.

This paper proposes an enhanced semi-supervised classification approach termed Nonnegative Sparse Neighborhood Propagation (SparseNP) that is an improvement to the existing neighborhood propagation due to the fact that the outputted soft labels of points cannot be ensured to be sufficiently sparse, discriminative, robust to noise and be probabilistic values. Note that the sparse property and strong discriminating ability of predicted labels is important, since ideally the soft label of each sample should have only one or few positive elements (that is, less unfavorable mixed signs are included) deciding its class assignment. To reduce the negative effects of unfavorable mixed signs on the learning performance, we regularize the l2,1-norm on the soft labels during optimization for enhancing the prediction results. The non-negativity and sum-to-one constraints are also included to ensure the outputted labels are probabilistic values. The proposed framework is solved in an alternative manner for delivering a more reliable solution so that the accuracy can be improved. Simulations show that satisfactory results can be obtained by the proposed SparseNP compared with other related approaches.