Large Language Models (LLMs) have demonstrated strong capabilities in web search and reasoning. However, their dependence on static training corpora makes them prone to factual errors and knowledge gaps. Retrieval-Augmented Generation (RAG) addresses this limitation by incorporating external knowledge sources, especially structured Knowledge Graphs (KGs), which provide explicit semantics and efficient retrieval. Existing KG-based RAG approaches, however, generally assume that anchor entities are accessible to initiate graph traversal, which limits their robustness in open-world settings where accurate linking between the user query and the KG entity is unreliable. To overcome this limitation, we propose AnchorRAG, a novel multi-agent collaboration framework for open-world RAG without the predefined anchor entities. Specifically, a predictor agent dynamically identifies candidate anchor entities by aligning user query terms with KG nodes and initializes independent retriever agents to conduct parallel multi-hop explorations from each candidate. Then a supervisor agent formulates the iterative retrieval strategy for these retriever agents and synthesizes the resulting knowledge paths to generate the final answer. This multi-agent collaboration framework improves retrieval robustness and mitigates the impact of ambiguous or erroneous anchors. Extensive experiments on four public benchmarks demonstrate that AnchorRAG significantly outperforms existing baselines and establishes new state-of-the-art results on the real-world reasoning tasks.

Survival analysis is extensively employed to analyze the probability of the event of interest, particularly in the medical field. Most current research treats patients as isolated entities, neglecting the complex associations among them, which leads to underutilization of valuable information. Recently, several studies address this limitation by incorporating patient graph structures. However, these approaches generally overlook two critical issues: 1) the exploration of heterogeneous inter-patient relationships, and 2) flexible and scalable inductive inference for test samples. To overcome these challenges, this study introduces a novel framework, Multiplex Graph Guided Deep Survival Analysis (MGG-Surv). Specifically, we employ multiplex patient graphs to capture comprehensive inter-patient associative information. Furthermore, we propose a teacher-student dual network architecture, where the teacher network encodes multiplex graphs, and the learned graph knowledge is transferred to the student network via a unidirectional connection termed Graph-Guided Distillation. The student network integrates this graph knowledge to predict survival outcomes without requiring the patient graphs. These innovative designs facilitate comprehensive integration of inter-patient relationships while achieving flexible and scalable graph-free inference. Experiments on four datasets, encompass-ing both single and competing risks, demonstrate the superior performance of our framework.

Graph Neural Networks (GNNs) have been proven effective in modeling graph data, mostly depending on the in-distribution assumption. While in the out-of-distribution (OOD) scenarios, especially for the more challenging node-level task, the feature and structure distribution shifts between training and test nodes lead to performance degradation. To improve node-level OOD generalization, typical approaches introduce graph augmentation to enrich the training environments and conduct invariant learning to learn stable representations across various augmented environments. However, their graph augmentations emphasize diversity but neglect the preservation of invariant patterns which are fundamental to invariant learning. Moreover, most of them simply conduct the classic invariant learning objective but lack the consideration of the graph-specific structure information. Therefore, to mitigate their weakness, we propose a Structure-aware Invariant learning framework for Node-level Graph OOD generalization (SING). Specifically, we develop the invariance constraint regularization terms during the optimization of augmentations. Additionally, we define the structure embedding to elucidate the structural property and design the structure embedding alignment loss to optimize the augmentations and the invariant representations. By introducing the structure information, we further integrate the unique structural property into invariant learning, thereby boosting the invariant message-passing GNNs. The extensive experiments on the transductive GOOD benchmark and the inductive datasets empirically validate our superior OOD generalization performance to baselines.

Multi-layer networks treat various types of interactions at each level to model complex systems in nature and society, and clustering of them is of great significance for revealing mechanisms of systems. Vast majority of current algorithms focus on identifying the common communities in clear multi-layer networks, and few attempt has been devoted to the detection of layer-specific communities in noised ones. To address these issues, a joint learning algorithm withGraphDenoising andStructureLearning (calledGDSL) for the detection of layer-specific communities in noised multi-layer networks is proposed, which simultaneously integrates graph denoising, structure learning, and module detection. To remove noise of networks, GDSL re-constructs affinity graphs for the original ones by preserving community structure. To enhance robustness and discriminative of features, GDSL explores the relations of features among various layers with the Hilbert-Schmidt Independence Criterion and structure learning. Finally, GDSL joins all these procedures with an objective function, and deduces optimization rules. The results show that GDSL not only significantly outperforms baselines but also enhances the robustness of the algorithm, providing an effective model for community detection in noised multi-layer networks.

Temporal link prediction on dynamic graphs has attracted considerable attention. Most methods focus on the graph at each timestamp and extract features for prediction. As graphs are directly compressed into feature matrices, the important latent information at each timestamp has not been well revealed. Eventually, the acquisition of dynamic evolution-related patterns is rendered inadequately. In this paper, inspired by the process of Super-Resolution (SR), a novel deep generative model SRG (Super Resolution Graph) is proposed. We innovatively introduce the concepts of the Low-Resolution (LR) graph, which is a single adjacent matrix at a timestamp, and the High-Resolution (HR) graph, which includes the link status of surrounding snapshots. Specifically, two major aspects are considered regarding the construction of the HR graph. For edges, we endeavor to obtain an extensive information transmission description that affects the current link status. For nodes, similar to the SR process, the neighbor relationship among nodes is maintained. In this form, we could predict the link status from a new perspective: Under the supervision of the graph moving average strategy, the conditional normalizing flow effectively realizes the transformation between LR and HR graphs. Extensive experiments on six real-world datasets from different applications demonstrate the effectiveness of our proposal.

Time series clustering has attracted growing attention due to the abundant data accessible and extensive value in various applications. The unique characteristics of time series, including high-dimension, warping, and the integration of multiple elastic measures, pose challenges for the present clustering algorithms, most of which take into account only part of these difficulties. In this paper, we make an effort to simultaneously address all aforementioned issues in time series clustering under a unified multiple kernels clustering (MKC) framework. Specifically, we first implicitly map the raw time series space into multiple kernel spaces via elastic distance measure functions. In such high-dimensional spaces, we resort to the tensor constraint based self-representation subspace clustering approach, which involves the self-paced learning paradigm, to explore the essential low-dimensional structure of the data, as well as the high-order complementary information from different elastic kernels. The proposed approach can be extended to more challenging multivariate time series clustering scenario in a direct but elegant way. Extensive experiments on 85 univariate and 10 multivariate time series datasets demonstrate the significant superiority of the proposed approach beyond the baseline and several state-of-the-art MKC methods.

In this paper, a novelDeepMulti-viewJointClustering (DMJC) framework is proposed, where multiple deep embedded features, multi-view fusion mechanism, and clustering assignments can be learned simultaneously. Through the joint learning strategy, the clustering-friendly multi-view features and useful multi-view complementary information can be exploited effectively to improve the clustering performance. Under the proposed joint learning framework, we design two ingenious variants of deep multi-view joint clustering models, whose multi-view fusion is implemented by two kinds of simple yet effective schemes. The first model, called DMJC-S, performs multi-view fusion in an implicit way via a novel multi-view soft assignment distribution. The second model, termed DMJC-T, defines a novel multi-view auxiliary target distribution to conduct the multi-view fusion explicitly. Both DMJC-S and DMJC-T are optimized under a KL divergence objective. Experiments on eight challenging image datasets demonstrate the superiority of both DMJC-S and DMJC-T over single/multi-view baselines and the state-of-the-art multi-view clustering methods, which proves the effectiveness of the proposed DMJC framework. To the best of our knowledge, this is the first work to model the multi-view clustering in a deep joint framework, which will provide a meaningful thinking in unsupervised multi-view learning.

Multi-class imbalanced problems have attracted growing attention from the real-world classification tasks in engineering. The underlying skewed distribution of multiple classes poses difficulties for learning algorithms, which becomes more challenging when considering overlapping between classes, lack of representative data, and mixed-type data. In this work, we address this problem in a data-oriented way. Motivated by a recently proposed over-sampling technique designed for numeric data sets, Mahalanobis Distance-based Over-sampling (MDO), we use this technique to capture the covariance structure of the minority class and to generate synthetic samples along the probability contours for learning algorithms. Based on MDO, we further improve the over-sampling strategy and generalize it for mixed-type data sets. The established technique, Adaptive Mahalanobis Distance-based Over-sampling (AMDO), introduces GSVD (Generalized Singular Value Decomposition) for mixed-type data, develops a partially balanced resampling scheme and optimizes the sample synthesis. Theoretical analysis is conducted to demonstrate the reasonability of AMDO. Extensive experimental testing is performed on 15 multi-class imbalanced benchmarks and two data sets for precipitation phase recognition in comparison with several state-of-the-art multi-class imbalanced learning methods. The results validate the effectiveness and robustness of our proposal.