Cross-domain sequential recommendation (CDSR) plays a critical role in decentralized Web applications by leveraging user behavior sequences across multiple platforms to alleviate data sparsity and capture dynamic preferences. However, existing federated CDSR frameworks face two fundamental challenges: (i) heterogeneous sequential interactions that encode domain-exclusive semantics and cannot be directly shared under privacy constraints, and (ii) strong trust assumptions that both servers and clients behave honestly, leaving federated training vulnerable to misreporting, malicious updates, and negative transfer. In this paper, we propose VeriFRL, a verifiable federated representation learning framework for cross-domain sequential recommendation. VeriFRL adopts a dual-module design that integrates representation learning with verifiable training: an attention-based variational encoder disentangles domain-shared and domain-exclusive representations to support transferable and privacy-preserving knowledge sharing, while a contribution evaluation module quantifies client-level and feature-level influences to enable verifiability, interpretability, and negative transfer detection. Extensive experiments on real-world multi-domain datasets demonstrate that VeriFRL achieves competitive or superior recommendation performance over state-of-the-art federated CDSR methods, while providing fine-grained insights into cross-domain knowledge transfer dynamics.

The Transformer model, with its ability to capture long-term dependencies, has demonstrated significant potential in enhancing long-term traffic flow prediction for effective urban transportation management. However, most existing Transformer-based methods adhere to a centralized approach, failing to address privacy concerns and to optimize computational resource utilization. Although emerging federated learning paradigms offer privacy protection, the average aggregation still overlooks client heterogeneity and lacks the synchronous efficiency required for traffic flow prediction tasks. Consequently, we introduce FedTFormer, a hierarchical federated learning framework tailored to boost the performance of Transformer models in decentralized environments, involving clients, edge servers, and a central server. Initially, clients are organized into clusters through a sophisticated static clustering mechanism anchored in bipartite graph theory. FedTFormer enhances robustness of Transformer by facilitating synchronous average aggregation within clusters. Additionally, it performs asynchronous fine-tuning of cluster-specific parameters, leveraging hypernetwork constructed on the central server. Clients utilize an optimized Transformer model for localized training, harnessing its proficiency in capturing long-term spatio-temporal dependencies. Ultimately, we conduct extensive experiments across three datasets, comparing our method against ten sophisticated approaches and demonstrating the effectiveness and robustness of FedTFormer.

Transformers, with their self-attention mechanisms and positional encoding, excel at modeling long-range dependencies. Such attribute has demonstrated significant potential in capturing complex disease patterns by integrating multimodal information, for example clinical notes and radiographs. However, their reliance on pre-trained deep neural networks to extract modality-specific features from large datasets makes them vulnerable to backdoor attacks, posing critical challenges for their deployment in healthcare applications. To address these vulnerabilities, we propose a robust multimodal Transformer-based framework, RMTrans, which mitigates the impact of malicious imaging data containing backdoor triggers while enhancing the model’s robustness. In the imaging data pre-processing stage, we introduce an efficient patch-based processing method that shifts the model’s focus toward learning global features rather than overfitting to localized (patch-level) patterns, thereby ensuring a more secure and reliable training process. Following this, we fuse multimodal representations and train a Vision Transformer (ViT) for disease prediction. Extensive experiments conducted on real-world datasets, including MIMIC-IV and MIMIC-CXR, validate the effectiveness of RMTrans. The proposed framework outperforms state-of-the-art baselines, demonstrating its potential as a secure and reliable solution for multimodal disease prediction.

Generating post hoc causal explanations for graph neural network-based recommender systems is vital for enhancing the credibility and interpretability of recommendations. Existing model-agnostic explainers primarily capture statistical correlations between topological information and recommendation outcomes. However, they often fail to identify true causal relationships due to their model-agnostic design and the challenges posed by heterogeneous graph structures. To address these limitations, we propose a causality-inspired graph neural network explainer for recommender systems, namely CaGE, which generates explanations reflecting causality in recommendation scenarios without accessing the internal parameters of the recommender system. Unlike previous explainers that rely on correlation-based learning, CaGE leverages heterogeneous interventional distributions to eliminate backdoor paths of non-causal variables in the structural causal model of the recommendation task, ensuring causation is accurately captured. Specifically, CaGE incorporates backdoor adjustment based on heterogeneous interventional distributions and causal contrastive learning to optimize a set of heterogeneous soft masks that disentangle causation from non-causation. Additionally, a causality-inspired meta-path search strategy is employed to represent causation as paths between users and recommended items, further enhancing explanation readability. Extensive experiments are conducted on three recommendation datasets, and the experimental results illustrate the superior fidelity of CaGE as compared to state-of-the-art baselines.

Network motifs, as a fundamental higher-order structure in large-scale networks, have received significant attention over recent years. Particularly in heterogeneous networks, motifs offer a higher capacity to uncover diverse information compared to homogeneous networks. However, the structural complexity and heterogeneity pose challenges in coding, counting, and profiling heterogeneous motifs. This work addresses these challenges by first introducing a novel heterogeneous motif coding method, adaptable to homogeneous motifs as well. Building upon this coding framework, we then propose GIFT, a heterogeneous network motif counting algorithm. GIFT effectively leverages combined structures of heterogeneous motifs through three key procedures: neighborhood searching, motif combination, and redundant motif filtering. We apply GIFT to count three-order and four-order motifs across eight distinct heterogeneous networks. Subsequently, we profile these detected motifs using four classical motif-based indicators. Experimental results demonstrate that by appropriately selecting motifs tailored to specific networks, heterogeneous motifs emerge as significant features in characterizing the underlying network structure.

Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network, graph attention network, graph autoencoder, and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field.

Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency.

In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science. Relying on the knowledge graph, it provides services for fast, accurate, and intelligent semantic querying as well as powerful recommendations. In addition, in order to realize information sharing, it provides open API to be served as the underlying architecture for advanced functions. Web of Scholars takes advantage of knowledge graph, which means that it will be able to access more knowledge if more search exist. It can be served as a useful and interoperable tool for scholars to conduct in-depth analysis within Science of Science.

With the rapid development of information and communication technologies, people are increasingly referring to web information to assist in their travel planning and decision making. Research shows that people conduct collaborative information searches while planning their travel activities online. However, little is known in depth about tourists' online collaborative search. This study examines tourists' collaborative information search behavior in detail, including their search stages, online search strategies, and information flow breakdowns. The data for analysis included pre‐ and postsearch questionnaires, web search and chat logs, and postsearch interviews. A model of tourist collaborative information retrieval was developed. The model identified collaborative planning, collaborative information searching, sharing of information, and collaborative decision making as four stages of tourists' collaborative search. The results show that tourists collaborated by planning their search strategies, dividing search tasks into subtasks and allocating workload, using search queries and URL links recommended by teammates, and discussing search results together. Related personal knowledge and experiences appeared important in trip planning and collaborative information search. During the collaborative search, tourists also encountered various information flow breakdowns in different search stages. These were classified and their effects on collaborative information search were reported. Implications for system design in support of collaborative information retrieval in travel contexts are also discussed.

Users often reformulate or modify their queries when they engage in searching information particularly when the search task is complex and exploratory. This paper investigates query reformulation behavior in collaborative tourism information searching on the Web. A user study was conducted with 17 pairs of participants and each pair worked as a team collaboratively on an exploratory travel search task in two scenarios. We analyzed users' collaborative query (CQ) reformulation behavior in two dimensions: firstly, CQ reformulation strategies; and secondly, the effect of individual queries and chat logs on CQ reformulation. The findings show that individual queries and chat logs were two major sources of query terms in CQ reformulation. The statistical results demonstrate the significant effect of individual queries on CQ reformulation. We also found that five operations were performed to reformulate the CQs, namely: addition, modification, reordering, addition and modification, and addition and reordering. These findings have implications for the design of query suggestions that could be offered to users during searches using collaborative search tools.