One-step bipartite graph cut: A normalized formulation and its application to scalable subspace clustering.

The bipartite graph structure has shown its promising ability in facilitating the subspace clustering and spectral clustering algorithms for large-scale datasets. To avoid the post-processing via k-means during the bipartite graph partitioning, the constrained Laplacian rank (CLR) is often utilized for constraining the number of connected components (i.e.

Structure-preserving contrastive graph clustering with dual-channel label alignment.

The past few years have witnessed the rapid development of contrastive graph clustering (CGC). Although a series of achievements have been made, there still remain two challenging problems in the literature. First, previous works typically generate different views via some pre-defined graph augmentation strategies, but inappropriate augmentations may alter the latent semantics of the original data.

Knowledge-aware Synergistic Discovery of Drug Combinations: A Large Language Model Perspective.

Drug combination therapy with significant advantages is a well-established concept in cancer treatment. Some related efforts have been made with multiple artful deep learning techniques. However, they are usually based on data for drug synergy prediction, ignoring the professional characteristics of data and the systematic knowledge accumulation.

A foundational triage system for improving accuracy in moderate acuity level emergency classifications.

Background: Triage is an essential part of Emergency Medicine, which may be assisted by AI models due to limited availability of medical staff. However, AI models for aiding triage have difficulty in identifying levels that are difficult or ambiguous for human clinicians to distinguish. This study aims to develop a more reliable triage model that improves the accuracy of classification, especially for cases with moderate acuity.

Dominant preference decoupling and guided perturbed preference injection for cross-domain sequence recommendation.

Cross-domain sequential recommendation jointly models cross- and intra-domain interaction sequences to extract relevant information to predict future interactions across domains. Nevertheless, current mainstream methods overlook the intra-domain dominant preference and the impact of perturbed preference on prediction outcomes. Hence, this paper proposes the Dominant Preference Decoupling and Guided Perturbed Preference Injection for Cross-Domain Sequence Recommendation (DP-CSR) model to address the aforementioned issues.

Enhancing partition distinction: A contrastive policy to recommendation unlearning.

With the growing privacy and data contamination concerns in recommendation systems, recommendation unlearning, i.e., unlearning the impact of specific learned data, has garnered more attention.

A pilot study for virus-induced neural network remodeling in sudden hearing loss and tinnitus: A graph attention network approach.

Purpose: This study aimed to investigate early central abnormalities in sudden sensorineural hearing loss (SSNHL) and tinnitus following viral infection, specifically associated with SARS-CoV-2. We sought to identify shared and distinct functional connectivity (FC) features across SSNHL and tinnitus patients with and without a history of SARS-CoV-2 infection and explore how virus influences brain network remodeling in SSNHL and tinnitus.

Methods: We recruited 31 SSNHL patients with tinnitus following SARS-CoV-2 infection (COV-SSNHL), 32 non-viral SSNHL patients with tinnitus, and 32 age- and gender-matched healthy controls (HC).

Multilevel Contrastive Multiview Clustering With Dual Self-Supervised Learning.

Multiview clustering (MVC) aims to integrate multiple related but different views of data to achieve more accurate clustering performance. Contrastive learning has found many applications in MVC due to its successful performance in unsupervised visual representation learning. However, existing MVC methods based on contrastive learning overlook the potential of high similarity nearest neighbors as positive pairs.

Sequence Notes: Characterization of the Near-Full-Length Genome of Two Novel Unique Recombinant Genome (CRF01_AE/CRF79_0107, CRF01_AE/CRF07_BC) in Hebei, China.

The global fight against human immunodeficiency virus (HIV) is complicated by its extensive genetic diversity, which arises from high mutation rates, rapid replication, and frequent recombination events. These factors lead to the emergence of numerous recombinant forms of HIV-1, contributing to the virus's adaptability and complicating prevention and treatment efforts. In this study, we identified two novel, unique recombinant forms (URFs) of HIV-1, CRF01_AE/CRF79_0107 and CRF01_AE/CRF07_BC, through near full-length genome sequence analysis.

Graph Prompt Clustering.

Due to the wide existence of unlabeled graph-structured data (e.g., molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups.

"Pre-Train, Prompt" Framework to Boost Graph Neural Networks Performance in EEG Analysis.

Electroencephalography (EEG) is a vital non-invasive technique used in neuroscience research and clinical diagnosis. However, EEG data have a complex non-Euclidean structure and are often scarce, making training effective graph neural network (GNN) models difficult. We propose a "pre-train, prompt" framework in graph neural networks for EEG analysis, called GNN-based EEG Prompt Learning (GEPL).

Learning to Imbalanced Open Set Generalize: A Meta-Learning Framework for Enhanced Mechanical Diagnosis.

To alleviate data distribution under different operating conditions, domain generalization (DG) has been applied in mechanical diagnosis. Still, its effectiveness is limited when unknown fault states appear in the target domain. Consequently, open set DG (OSDG) has emerged to identify unknown classes in unknown domains.

Contrastive Ensemble Clustering.

Ensemble clustering aims to combine different base clusterings into a better clustering than that of the individual one. In general, a co-association matrix depicting the pairwise affinity between different data samples is constructed by average fusion or weighted fusion of the connective matrices from multiple base clusterings. Despite the significant success, the existing works fail to capture the global structure information from multiple noisy connective matrices.

Interpretable Staging Prediction of Liver Cancer Based on Joint-Knowledge Network.

Clinical staging is crucial for treatment strategies and improving 5-year survival rates in hepatocellular carcinoma (HCC) patients. However, existing methods struggle to distinguish stages with highly similar textual features. Additionally, their lack of interpretability hampers their practical application in medical scenarios.

Knowledge-Reinforced Cross-Domain Recommendation.

Over the past few years, cross-domain recommendation has gained great attention to resolve the cold-start issue. Many existing cross-domain recommendation methods model a preference bridge between the source and target domains to transfer preferences by the overlapping users. However, when there are insufficient cross-domain users available to bridge the two domains, it will negatively impact the recommender system's accuracy (ACC) and performance.

Pyramid contrastive learning for clustering.

With its ability of joint representation learning and clustering via deep neural networks, the deep clustering have gained significant attention in recent years. Despite the considerable progress, most of the previous deep clustering methods still suffer from three critical limitations. First, they tend to associate some distribution-based clustering loss to the neural network, which often overlook the sample-wise contrastiveness for discriminative representation learning.

Motif-aware curriculum learning for node classification.

Node classification, seeking to predict the categories of unlabeled nodes, is a crucial task in graph learning. One of the most popular methods for node classification is currently Graph Neural Networks (GNNs). However, conventional GNNs assign equal importance to all training nodes, which can lead to a reduction in accuracy and robustness due to the influence of complex nodes information.

Interaction-knowledge semantic alignment for recommendation.

In order to alleviate the issue of data sparsity, knowledge graphs are introduced into recommender systems because they contain diverse information about items. The existing knowledge graph enhanced recommender systems utilize both user-item interaction data and knowledge graph, but those methods ignore the semantic difference between interaction data and knowledge graph. On the other hand, for the item representations obtained from two kinds of graph structure data respectively, the existing methods of fusing representations only consider the item representations themselves, without considering the personalized preference of users.

MIGP: Metapath Integrated Graph Prompt Neural Network.

Graph neural networks (GNNs) leveraging metapaths have garnered extensive utilization. Nevertheless, the escalating parameters and data corpus within graph pre-training models incur mounting training costs. Consequently, GNN models encounter hurdles including diminished generalization capacity and compromised performance amidst small sample datasets.

Tensorized Incomplete Multi-view Kernel Subspace Clustering.

Recently considerable advances have been achieved in the incomplete multi-view clustering (IMC) research. However, the current IMC works are often faced with three challenging issues. First, they mostly lack the ability to recover the nonlinear subspace structures in the multiple kernel spaces.

Fine-Grained Learning Behavior-Oriented Knowledge Distillation for Graph Neural Networks.

Knowledge distillation (KD), as an effective compression technology, is used to reduce the resource consumption of graph neural networks (GNNs) and facilitate their deployment on resource-constrained devices. Numerous studies exist on GNN distillation, and however, the impacts of knowledge complexity and differences in learning behavior between teachers and students on distillation efficiency remain underexplored. We propose a KD method for fine-grained learning behavior (FLB), comprising two main components: feature knowledge decoupling (FKD) and teacher learning behavior guidance (TLBG).