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.
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Large-scale multimodal contrastive learning has recently achieved impressive success in learning rich and transferable representations, yet it remains fundamentally limited by the uniform treatment of feature dimensions and the neglect of the intrinsic spectral structure of the learned features. Empirical evidence indicates that high-dimensional embeddings tend to collapse into narrow cones, concentrating task-relevant semantics in a small subspace, while the majority of dimensions remain occupied by noise and spurious correlations. Such spectral imbalance and entanglement undermine model generalization. We propose Spectral Disentanglement and Enhancement (SDE), a novel framework that bridges the gap between the geometry of the embedded spaces and their spectral properties. Our approach leverages singular value decomposition to adaptively partition feature dimensions into strong signals that capture task-critical semantics, weak signals that reflect ancillary correlations, and noise representing irrelevant perturbations. A curriculum-based spectral enhancement strategy is then applied, selectively amplifying informative components with theoretical guarantees on training stability. Building upon the enhanced features, we further introduce a dual-domain contrastive loss that jointly optimizes alignment in both the feature and spectral spaces, effectively integrating spectral regularization into the training process and encouraging richer, more robust representations. Extensive experiments on large-scale multimodal benchmarks demonstrate that SDE consistently improves representation robustness and generalization, outperforming state-of-the-art methods. SDE integrates seamlessly with existing contrastive pipelines, offering an effective solution for multimodal representation learning.
Knowledge Tracing (KT) is pivotal in intelligent tutoring systems, as it models the dynamic evolution of student knowledge from their learning interactions. However, the cross-disciplinary generalization of existing KT models is subjected to a dual constraint: the heterogeneity in students' cognitive abilities and the divergent disciplinary-specific knowledge structures.To address this challenge, we propose DTransKT, a dual transferable knowledge tracing framework tailored for cross-disciplinary adaptability. DTransKT enhances existing knowledge tracing models by dynamically aligning student representations and integrating external knowledge semantics.Specifically, the framework incorporates a Cross-disciplinary Graph-matching (CG) module, which captures meta-skill representations based on students' learning trajectories. Through cross-disciplinary node matching, the CG module aligns student-specific features, thereby improving tracing accuracy. Additionally, the Cross-disciplinary Attention-assisting (CA) module leverages pre-trained language models to extract meta-semantic from textual content, enhancing transferability.Extensive experimental evaluations demonstrate that DTransKT consistently enhances the performance of seven prominent KT models under direct transfer settings, achieving average improvements of 14.2% in accuracy (ACC) and 4.5% in area under the curve (AUC) across diverse datasets. These findings affirm the efficacy of our approach in enabling cross-disciplinary transfer for knowledge tracing. Code and pre-trained models are available at: https://github.com/Dual-KT/DTransKT.
The Web is a rich source of structured data in the form of tables, from product catalogs and knowledge bases to scientific datasets. However, the heterogeneity of the structure and semantics of these tables makes it challenging to build a unified method that can effectively leverage the information they contain. Meanwhile, Large language models (LLMs) are becoming an increasingly integral component of web infrastructure for tasks like semantic search. This raises a crucial question: can we leverage these already-deployed LLMs to classify structured data in web-native tables (e.g., product catalogs, knowledge base exports, scientific data portals), avoiding the need for specialized models or extensive retraining? This work investigates a lightweight paradigm, Table Representation with Language Model (TaRL), for few-shot tabular classification that directly utilizes semantic embeddings of individual table rows. We first show that naive application of these embeddings underperforms compared to specialized tabular models. We then demonstrate that their potentials can be unlocked with two key techniques: removing the common component from all embeddings and calibrating the softmax temperature. We show that a simple meta-learner, trained on handcrafted features, can learn to predict an appropriate temperature. This approach achieves performance comparable to state-of-the-art models in low-data regimes (k ? 32) of semantically-rich tables. Our findings demonstrate the viability of reusing existing LLM infrastructure for efficient semantics-driven pathway to reuse existing LLM infrastructure for Web table understanding.
Continual knowledge graph embedding (CKGE) aims to incrementally learn embeddings of entities and relations in a knowledge graph (KG) that evolves over time with a sequence of newly arriving triples. This capability is essential for dynamic applications such as retrieval-augmented generation (RAG), but remains challenging due to the structural diversity and non-uniform growth of evolving KGs. Existing CKGE approaches reveal limitations in both leveraging rich structural information and achieving update efficiency, as they often rely on simplistic metrics (e.g., degree) and a vanilla TransE loss that is not adaptive to structural dynamics. In this work, we propose STARK (Structure-aware and Adaptive Representation learning for CKGE), a fast yet effective CKGE framework that enhances structure awareness and supports adaptive optimization. To this end, we propose two major techniques, namely structural novelty prioritization (SNP) and adaptive TransE loss (ATL). Through SNP, STARK allocates higher representational capacity to topologically more important entities, while ATL adaptively keeps embeddings close to the true target of each head-relation pair (h, r, ?), dynamically scaling the boundary according to the cardinality of candidate tails. Extensive experiments on multiple CKGE benchmarks demonstrate that STARK achieves both higher accuracy and better efficiency in the time-performance tradeoff compared to existing state-of-the-art methods. Moreover, embedding visualizations and quantitative analysis confirm that STARK produces more coherent clusters of entities sharing the same (h, r, ?), reflecting improved structural consistency.
Accurate travel time estimation (TTE) plays a crucial role in intelligent transportation systems. However, it remains challenging due to heterogeneous data sources and complex traffic dynamics. Moreover, traditional approaches typically convert trajectory data into fixed-length representations. This overlooks the inherent variability of real-world motion patterns, often resulting in information loss and redundancy. To address these challenges, this paper introduces the Multimodal Dynamic Trajectory Integration (MDTI) framework--a novel multimodal trajectory representation learning approach that integrates GPS sequences, grid trajectories, and road network constraints to enhance the performance of TTE. MDTI employs modality-specific encoders and a multimodal fusion module to capture complementary spatial, temporal, and topological semantics, while a dynamic trajectory modeling mechanism adaptively regulates information density for trajectories of varying lengths. Two self-supervised pretraining objectives, named contrastive alignment and masked language modeling, further strengthen multimodal consistency and contextual understanding. Extensive experiments on three real-world datasets demonstrate that MDTI consistently outperforms state-of-the-art baselines, confirming its robustness and strong generalization abilities. The code is publicly available at: https://github.com/City-Computing/MDTI.
Recently, hypergraph knowledge distillation has been proposed to alleviate the high computational cost of Hypergraph Neural Networks (HGNNs) when modeling high-order relationships in Web-related graph tasks. Its effectiveness primarily depends on the quality of knowledge transferred from the teacher and the representation capability of the student. However, existing methods remain limited on both sides. On the teacher side, most methods typically rely on a single HGNN teacher, which provides limited structural and semantic knowledge, thereby constraining the upper bound of the student's performance. The potential of exploiting multiple teachers in HGNNs remains largely underexplored. On the student side, existing methods ignore the student's capability to capture high-order semantic and structural information beyond simply imitating teacher outputs, leading to limited representation learning. To address these limitations, we propose MARCH, a framework for Multi-TeAcheR Contrastive Hypergraph Distillation, which advances semantic modeling and distillation for Web-scale structured data. Specifically, MARCH proposes a multi-teacher distillation strategy that adaptively transfers complementary knowledge from multiple teachers at both node and hyperedge levels, empowering the student model to learn richer and more discriminative representations and even outperform its teachers. Extensive experiments on six benchmark datasets demonstrate the superior performance of MARCH.
Real-world data streams exhibit inherent non-stationarity characterized by concept drift, posing significant challenges for adaptive learning systems. While existing methods address isolated distribution shifts, they overlook the critical co-evolution of label spaces and distributions under limited supervision and persistent uncertainty. To address this, we formalize Generalized Incremental Learning under Concept Drift (GILCD), characterizing the joint evolution of distributions and label spaces in open-environment streaming contexts, and propose a novel framework called Calibrated Source-Free Adaptation (CSFA). First, CSFA introduces a training-free prototype calibration mechanism that dynamically fuses emerging prototypes with base representations, enabling stable new-class identification without optimization overhead. Second, we design a novel source-free adaptation algorithm, i.e., Reliable Surrogate Gap Sharpness-aware (RSGS) minimization. It integrates sharpness-aware perturbation loss optimization with surrogate gap minimization, while employing entropy-based uncertainty filtering to discard unreliable samples. This mechanism ensures robust distribution alignment and mitigates generalization degradation caused by uncertainties. Thus, CSFA establishes a unified framework for stable adaptation to evolving semantics and distributions in open-world streaming scenarios. Extensive experiments validate the superior performance and effectiveness of CSFA compared to SOTA approaches.
Pseudo-Alignment is a pervasive challenge in many large language models for time series (LLM4TS) models, often causing them to underperform compared to linear models or randomly initialised backbones. However, there is limited discussion in the community for the reasons that pseudo-alignment occurs. In this work, we conduct a thorough investigation into the root causes of pseudo-alignment in LLM4TS and build a connection of pseudo-alignment to the cone effect in LLM. We demonstrate that pseudo-alignment arises from the interplay of cone effect within pretrained LLM components and the intrinsically low-dimensional manifold of time-series data. In addition, we also introduce TimeSUP, a novel technique designed to mitigate this issue and improve forecast performance in existing LLM4TS approaches. TimeSUP addresses this by increasing the time series manifold to more closely match the intrinsic dimension of language embeddings, allowing the model to distinguish temporal signals clearly while still capturing shared structures across modalities. As a result, representations for time and language tokens remain distinct yet exhibit high cosine similarity, signifying that the model preserves each modality's unique features while learning their commonalities in a unified embedding space. Empirically, TimeSUP consistently outperforms state-of-the-art LLM4TS methods and other lightweight baselines on long-term forecasting performance. Furthermore, it can be seamlessly integrated into four existing LLM4TS pipelines and delivers significant improvements in forecasting performance.
The explosive growth of multimodal web data demands communication that transmits meaning rather than raw bits. Existing semantic-communication systems often fail under noise, missing modalities, and distribution shifts because they optimize surface features instead of modality-invariant knowledge. We present Grasp, a knowledge-centric framework for cross-modal communication. Grasp segments streams into semantic blocks and builds a graph over them; a lightweight Graph Neural Networks (GNN) produces schedulable, importance-weighted representations. At its core is knowledge purification : we minimize a conditional mutual information upper bound to perform a three-way disentanglement—strongly related, weakly related, and task-irrelevant components—so that only essential semantics are transmitted while non-essential factors are suppressed. To maintain synchrony, we introduce one-to-two temporal contrastive learning to achieve triple alignment of video, audio, and text despite sampling asynchrony. For efficient transmission, Grasp uses a cross-modal shared vector-quantization codebook—a discrete knowledge codebook —updated by multimodal attention. At the receiver, a soft-recovery mechanism leverages this shared knowledge to robustly reconstruct semantics under low signal-to-noise ratio (SNR) or missing modalities, yielding graceful degradation. Across web tasks—including cross-modal retrieval and missing-modality inference—Grasp improves knowledge consistency, semantic fidelity, and downstream performance over strong baselines while maintaining low latency. These results show that communication structured around purified knowledge is key to building robust, semantic-aware systems for the modern web.
Text classification is a crucial and fundamental task in web content mining. Compared with the previous learning paradigm of pre-training and fine-tuning by cross entropy loss, the recently proposed supervised contrastive learning approach has received tremendous attention due to its powerful feature learning capability and robustness. Although several studies have incorporated this technique for text classification, some limitations remain. First, many text datasets are imbalanced, and the learning mechanism of supervised contrastive learning is sensitive to data imbalance, which may harm the model's performance. Moreover, these models leverage separate classification branches with cross entropy and supervised contrastive learning branches without explicit mutual guidance. To this end, we propose a novel model named SharpReCL for imbalanced text classification tasks. First, we obtain the prototype vector of each class in the balanced classification branch to act as a representation of each class. Then, by further explicitly leveraging the prototype vectors, we construct a proper and sufficient target sample set with the same size for each class to perform the supervised contrastive learning procedure. The empirical results show the effectiveness of our model, which even outperforms popular large language models across several datasets. Our code is available https://github.com/KEAML-JLU/SharpReCL
Multi-modal knowledge graphs (MMKGs) enrich traditional knowledge graphs by incorporating heterogeneous modalities such as textual descriptions and visual content, offering complementary semantic cues for knowledge reasoning. However, existing approaches often overlook the structural dependencies within each modality, apply static or coarse-grained fusion strategies, and insufficiently model relational semantics. We propose a Multi-Granularity Multi-Modal Knowledge Graph Representation Learning Method via Subgraph-aware Adaptive Fusion and Hierarchical Relation Modeling (SAFER ), which implement multi-modal knowledge representation through adaptive fusion of multi-granularity information such as multi-modal semantics, knowledge structures and relations. SAFER explicitly constructs modality-specific subgraphs and employs structure-aware graph attention networks to effectively capture intra-modal structural dependencies. We propose an adaptive multi-modal fusion mechanism, which aggregates modality-specific embeddings at the semantic level by dynamically assigning entity-specific modality weights. We design a two-stage multi-granularity knowledge relation modeling strategy, which utilizes a structure-aware multi-modal adaptive pre-fusion to preserve topological information and a relation-aware graph attention network (RGAT) post-fusion to encode relational semantics. Extensive experiments on several benchmark datasets demonstrate that the proposed SAFER significantly outperforms competitive baselines on link prediction and relation reasoning tasks.
Graph-structured data is foundational to numerous web applications, and watermarking is crucial for protecting their intellectual property and ensuring data provenance. Existing watermarking methods primarily operate on graph structures or entangled graph representations, which compromise the transparency and robustness of watermarks due to the information coupling in representing graphs and uncontrollable discretization in transforming continuous numerical representations into graph structures. This motivates us to propose DRGW, the first graph watermarking framework that addresses these issues through disentangled representation learning. Specifically, we design an adversarially trained encoder that learns an invariant structural representation against diverse perturbations and derives a statistically independent watermark carrier, ensuring both robustness and transparency of watermarks. Meanwhile, we devise a graph-aware invertible neural network to provide a lossless channel for watermark embedding and extraction, guaranteeing high detectability and transparency of watermarks. Additionally, we develop a structure-aware editor that resolves the issue of latent modifications into discrete graph edits, ensuring robustness against structural perturbations. Experiments on diverse benchmark datasets demonstrate the superior effectiveness of DRGW.
Website Fingerprinting (WF) attacks aim to infer the websites visited by Tor users by analyzing patterns in encrypted network traffic. However, most existing WF attacks are evaluated on traffic collected in controlled environments with fixed configurations, failing to reflect the complexity and variability of real-world conditions. In practice, traffic is far more dynamic and diverse due to heterogeneous network conditions, the large number of subpages within individual websites, and continuous evolution of website content. These factors increase intra-class variability and induce temporal feature drift, which ultimately degrades the long-term effectiveness of existing attacks. In this paper, we propose TraVerse, an LLM-based representation learning framework designed to achieve robust WF attacks under real-world conditions. TraVerse applies architectural adaptation and large-scale fine-tuning on diverse unlabeled traffic to learn generalizable and resilient representations that remain effective in dynamic and evolving environments. Furthermore, TraVerse integrates a lightweight classifier atop the LLM-derived representations, enabling accurate website identification and efficient few-shot adaptation with minimal model updates. We prototype TraVerse and conduct comprehensive evaluations using real-user traffic. Experimental results show that TraVerse improves Accuracy@3 by an average of 176.3% and weighted F1 by 343.3% over state-of-the-art baselines, while maintaining strong performance throughout a three-month longitudinal evaluation.
Graph Neural Networks (GNNs) have become a pivotal framework for modeling graph-structured data, enabling a wide range of applications from social network analysis to molecular chemistry. By integrating large language models (LLMs), text-attributed graphs (TAGs) enhance node representations with rich textual semantics, significantly boosting the expressive power of graph-based learning. However, this synergy introduces critical vulnerabilities in both topology and text. Although specialized attack methods have been designed for each of these aspects, no work has yet unified them into a comprehensive approach. In this work, we propose the Interpretable Multi-Dimensional Graph Attack (IMDGA), a human-centric framework orchestrating multi-level perturbations across graph structure and textual features. IMDGA utilizes three tightly integrated modules to craft attacks that balance interpretability and impact, enabling a deeper understanding of Graph-LLM vulnerabilities. Through rigorous theoretical analysis and comprehensive empirical evaluations on diverse datasets and architectures, IMDGA demonstrates superior interpretability, attack effectiveness, stealthiness, and robustness compared to existing methods. By exposing these underexplored semantic vulnerabilities, our work offers valuable insights for improving Graph-LLM resilience. Our code is available at https://github.com/bwfan-bit/IMDGA.
The security of web services increasingly relies on accurate detection of advanced, previously unseen attacks hidden within complex host activities. Provenance-based intrusion detection systems (PIDSes) offer a promising foundation for this task by capturing rich causal and structural relationships across processes, files, and network interactions. However, recent studies show that these graph-driven methods remain vulnerable to graph manipulation attacks, where adversaries subtly alter provenance graphs to evade detection, which limits their practical deployment. To address this challenge, we present ProvGuard, a robust anomaly detection framework that couples logic-aware multi-view augmentation with contrastive representation learning. Instead of applying arbitrary structural perturbations, ProvGuard employs Logic-Aware Noise Injection (LNI) to generate semantically valid graph views that preserve the causal semantics of provenance data. These views are then leveraged in a Logic-Preserving Contrastive Learning module, enabling the model to learn representations invariant to benign transformations yet sensitive to adversarial inconsistencies. Extensive evaluations on multiple provenance datasets show that ProvGuard surpasses state-of-the-art detectors in resisting graph manipulation attacks while maintaining high detection accuracy and efficiency, achieving an average F1-score above 96% with less than a 10% AUC drop.
The advent of machine learning as a service (MLaaS) has necessitated secure multi-party computation (MPC)-based private inference (PI) to address the privacy concerns that arise when web servers offer query inference services to users. However, the formal privacy protection incurs substantial communication and latency overheads, particularly for large models such as vision transformers (ViTs). Existing methods either ignore the inherent attention dependencies or naively extend CNN optimizations to ViTs despite their structural discrepancies, resulting in sub-optimal performance. In this paper, we co-design the MPC and architectural properties of ViT and propose SecViT, an efficient and secure inference framework that automatically adapts ViTs into privacy-friendly counterparts with optimal attention configurations at different layers and tokens under MPC, balancing model capability and efficiency. SecViT features an MPC-efficient, layer-dependent and load-balancing attention representation adapter to facilitate feature reuse across multiple highly correlated layers without impacting accuracy. To further reduce the inference cost, SecViT also develops fine-grained composite attention and activation approximation algorithms to achieve superior accuracy-efficiency trade-offs. Experiments show that SecViT reduces communication by 6.0x and latency by 4.6x with iso-accuracy over MPCViT, and improves accuracy by 4.92% with 1.6x lower latency over PriViT on Tiny-ImageNet. Compared with state-of-the-art PI protocols, SecViT further achieves 10.0x communication saving and 7.9x latency reduction over BumbleBee.
Web tracking is increasingly pervasive, raising serious concerns about user privacy and security. Among existing techniques, pixel tracking is particularly stealthy and cost-effective, embedding invisible images that exfiltrate user activities to third-party servers. Current defenses, including filter list blocking and conventional machine learning, often fail to capture the cross-site associations that enable pixel tracking to evade detection. To address this limitation, we introduce TGNN, a framework that formulates pixel tracking detection as an edge classification task on a Tracking Directed Graph (TDG), which models third-party associations across websites. TGNN encodes HTTP traffic into structured quadruples and learns both semantic features and interaction patterns. To overcome the scarcity of reliable labels, we propose a large language model (LLM)-based annotation method that leverages minimal expert supervision to produce high-quality labels, significantly improving detection. Experiments conducted on traffic from the Alexa top-10K websites demonstrate that TGNN substantially outperforms existing baselines, while the LLM-based annotation achieves accuracy comparable to expert curation. Our large-scale measurement reveals that at least 16.74% of websites engage in pixel tracking via major third-party infrastructures, establishing cross-domain tracking as a pervasive practice in the wild and indicating a potential privacy threat in the modern Web ecosystem.
Federated knowledge graph embedding (FKGE) leverages distributed knowledge graphs (KGs) to collaboratively learn latent representations while preserving data privacy. However, this decentralized paradigm exposes vulnerabilities to client-side attacks, particularly untargeted poisoning attacks, which remain underexplored in FKGE and risk severe performance degradation. This paper presents the first systematic investigation of untargeted poisoning attacks on FKGE, unveiling their feasibility at both data and embedding levels. We design three novel poisoning attacks, among which the embedding-level attacks exhibit superior attack efficacy. To mitigate such threats, we further propose a learning-based defense mechanism that leverages embedding density for anomaly detection to identify malicious clients. Our theoretical results substantiate the effectiveness of both the attacks and the defense, and clarify why embedding-level poisoning is especially potent. Extensive experiments demonstrate that the proposed attacks induce significant performance deterioration, with core metrics dropping by up to 93%. Conversely, LDM effectively counters these attacks and restores performance to pre-attack levels or even surpasses them when mitigating embedding-level attacks.
Knowledge Graph Question Answering (KGQA) leverages struc- tured knowledge graphs for reliable reasoning. Graph-based Retrieval- Augmented Generation (RAG) addresses the incompleteness and hallucination issues of Large Language Models (LLMs) by retrieving query-relevant subgraphs. However, existing approaches rely on single-intent semantic retrieval, compressing queries into single representations and optimizing each query independently. This leads to narrow triple selection that omits complementary information. While multi-intent retrieval diversification addresses this limitation, it faces critical challenges: (1) semantic diversification does not guarantee reasoning performance, and (2) co-selection frequency across intents does not ensure reasoning benefit. We propose Topic-Adaptive Retrieval Diversification (TARD), based on end-to-end optimization via generation feedback. TARD adaptively extracts multiple topic-based intents through neural topic modeling and employs a Gumbel-Softmax differentiable sampling to enable joint optimization. Supervised fine-tuning aligns the topic-adaptive multi-intent selector and triple scorer with reasoning performance to achieve beneficial consensus patterns. Adaptive direct preference optimization trains the generator to utilize relevant consensus while ignoring uninformative patterns. Experiments on WebQSP and CWQ show that TARD outperforms state-of-the-art baselines. Our code and data are publicly available at https://github.com/leedongcheon/TARD.