Attributed heterogeneous information networks (AHINs) encode rich semantics through diverse node and edge types. Recent learning-based community search methods on AHINs have shown promising performance but face two major limitations: i) difficulty scaling to large graphs due to memory-intensive neighbor-based propagation (e.g., GNNs and node-level attention), and ii) reliance on explicit community-level labels, which are often unavailable or costly to obtain. To address these issues, we propose a scalable Semi-supervised Community Search framework on AHINs (SCSAH), enabling scalability and efficiency, while eliminating the need for community-level labels by leveraging readily available node classification labels. Specifically, we devise MvSF2Token to extract Multi-view Semantic Features (MvSFs) as compact subgraph-level tokens before training, significantly reducing model propagation complexity. We then design a View-Aware Semantic Graph Transformer (VASGhormer) to effectively encode MvSFs by capturing cross-view dependencies and fusing semantic features. The combination of MvSF2Token and VASGhormer ensures scalability, efficiency, and robust performance. Furthermore, we design a View-Aware Contrastive Learner to train VASGhormer without requiring community-level supervision. Extensive experiments on five real-world datasets show that SCSAH outperforms state-of-the-art methods, achieving 18.06% higher performance and 10.43 times faster training.

Multimodal data fusion involves integrating and analyzing information from multiple modalities to uncover latent correlations and complementary patterns, thereby enhancing data processing and decision-making. While existing methods for structured multimodal inputs are typically designed around specific tasks and assume fully observed modalities, realworld applications often suffer from uncertain or missing modality inputs due to various factors. Some traditional models overly emphasize local interactions within missing modalities, neglecting the global complementary cues embedded in multimodal representations. To overcome these limitations, we propose a Dynamic Multimodal Data Fusion model based on Contrastive Learning (CL-DMDF). CL-DMDF introduces a novel attention mechanism that operates across both feature and modality dimensions to compute reliable attention scores, effectively reflecting importance at each level. The CL-DMDF further incorporates an entity-centroid contrastive learning module that constructs centroid-based positive samples from entity features to enhance discriminative learning. Additionally, an adaptive fusion module is employed to improve the efficiency and accuracy of dynamic fusion strategies. Extensive experiments conducted on three datasets demonstrate the effectiveness of the CL-DMDF across diverse multimodal fusion tasks.

Community search is a fundamental graph-based retrieval problem that aims to identify a query-dependent subgraph whose nodes exhibit strong internal connectivity. While recent learning-based methods improve retrieval effectiveness via graph representation learning, they follow a ''one-use-one-train'' paradigm that requires retraining or fine-tuning for each target graph, leading to high data dependency, high training costs, and limited generalization. To handle this, we propose OFA-CS, a ''one-for-all'' community search framework trained once on source datasets and directly deployed to arbitrary unseen graphs without retraining or fine-tuning, while preserving strong performance. Specifically, we introduce a Spectral-Aware Feature Alignment module to unify feature dimensionality and align cross-domain semantics in a community-aware manner. We further develop a Graph Diffusion Tokenized Transformer that constructs hybrid token sequences from local and global structural contexts for Transformer encoding, and applies diffusion-based refinement to mitigate distribution shifts on unseen graphs. With the unified representations, communities are efficiently retrieved via a modularity-driven search procedure. Extensive experiments on diverse real-world graphs demonstrate that OFA-CS achieves strong cross-domain generalization and competitive retrieval effectiveness against state-of-the-art methods, without requiring target-domain supervision.

Next point-of-interest (POI) recommendation plays a crucial role in enhancing user travel experiences and driving platform revenues by suggesting potentially appealing locations to users. Recent advancements have focused on capturing the general preferences and dynamic interests of users by modeling long- and short-term trajectories. However, existing long-term models struggle to accurately capture periodic user behaviors beyond simple distinctions such as weekdays/weekends or seasons. Meanwhile, short-term models often follow the assumption that users prefer to visit nearby locations, thereby overlooking the personalized spatial preferences. Furthermore, the interaction between the long- and short-term preferences remains underexplored. To address these gaps, we propose a novel model: personalized short-term and periodic long-term preferences modeling with contrastive learning for next POI recommendation. This model leverages the inherent similarities between a user’s periodic long-term and distance-based short-term preferences while distinguishing the travel preferences of different users, ultimately improving the accuracy of next POI predictions. Specifically, we introduce a spatial span graph (S${}^{2}$graph) to model the personalized distance span preferences. Additionally, we employ Mamba-based and discrete wavelet transform-based methods to capture long-term periodic patterns. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed model.

Entity alignment (EA) aims to identify equivalent entities across different knowledge graphs (KGs). While existing approaches leverage KG neighborhood structures for alignment, they often fail to effectively distinguish relevant from irrelevant neighbors due to insufficient handling of neighbor heterogeneity. Additionally, entity enhancement strategies remain underutilized. To address these issues, we propose a novel Relationship Structure Line Graph for Semi-supervised Entity Alignment Based on Edge Weight Adjustment, named RSGEA. It first enhances entity representations by deeply analyzing relational connectivity structures in KGs, capturing key relational information from second-order and triangular-ring structures. It then employs an attention mechanism to dynamically adjust edge weights, mitigating the impact of noisy edges during information propagation. Finally, we employ the Sinkhorn algorithm to refine the similarity matrix, improving alignment accuracy. Furthermore, we introduce an unsupervised version to accommodate diverse scenarios. Extensive experiments on five cross-lingual datasets validate the effectiveness and robustness of the RSGEA, demonstrating significant performance improvements.

A multigraph is modeled as a bag of graphs. Exact multigraph matching search aims to find all multigraphs that are the same as the query multigraphs from the data multigraph datasets. To the best of our knowledge, works regarding exact multigraph matching search have not been reported although they have a very wide range of application scenarios. In this article, we propose an efficient algorithm to solve the problem of exact multigraph matching search. We first propose a definition of exact multigraph matching and its Basic Method (BM), called BM, which has a considerable amount of graph isomorphism detection calculations and, thus, has very high computational complexity. Obviously, it is impractical to compare the query multigraph to each data multigraph in the multigraph datasets. To reduce the search space, multiple filtering conditions are proposed to obtain a candidate result set containing all the final results, including the cardinality filter, the vertex filter, the edge filter, the size filter, and the star filter. Then, each multigraph in the candidate result set is verified with the Improved BM (IBM) algorithm. Moreover, an offline and Multilayer Inverted Index (MII), named MII, is proposed to further accelerate the search process. Finally, we propose an Exact Multigraph Matching Search (EMMS) algorithm, based on the abovementioned technologies. We also analyze its time complexity. Extensive experiments on real datasets demonstrate the effectiveness and efficiency of the proposed algorithms.

At present, event-based social network (EBSN) platforms are becoming more and more popular, which main function is to arrange appropriate social activities for interested users. The existing methods usually assume that each user can participate in a limited number of events and solve the spatio-temporal conflicts caused by the limited number of events. However, in practical applications, the existing methods emerge the following problems: (1) they don't estimate the time cost caused by travel distance; (2) the constraint of the limiting number of users participating events and the schedule of users is not accurate enough. Therefore, first, we combine the position information and propose RDP algorithm to provide personalized event planning based on considering the free time of users, the average moving speed of users, the interest value of users as a whole, which ensures the approximate ratio of our algorithm. Second, we present RGPV and the RGPT algorithms to reduce the running time and improve the efficiency of time and space, so as to ensure each user can participate in the events on time. Finally, the experiments based on the real dataset can show that the proposed algorithms are effective and efficient.