This paper introduces Vistar, a novel self-supervised framework for inertial measurement unit (IMU) signal perception designed for large language models (LLMs). Unlike visual data, IMU signals are high-frequency time series with low interpretability, making manual annotation with natural language particularly challenging. Even when using vision-language models (VLMs) to describe events in videos synchronized with IMU signals, a semantic gap remains between high-level visual semantics and low-level IMU vibrations. The core idea of Vistar is to achieve accurate IMU signal perception through collaborations between offline cross-modal alignment and online retrieval-augmented generation. During offline training, Vistar uses pretrained vision and language encoders as anchors to learn IMU encoders via hierarchical cross-modal contrastive learning, establishing both inter- and intra-sample alignment. Given that the enhanced training strategy still fails to achieve precise alignment between IMU and text, during online inference, Vistar further employs a retrieval-augmented generation mechanism to generate distilled textual descriptions from similar text filtered based on structural relations of their paired IMU samples. Extensive evaluations on three multimodal datasets demonstrate that Vistar consistently outperforms state-of-the-art (SOTA) baselines by up to 57.45% in IMU-to-text retrieval and improves the generated text similarity with ground truths in IMU perception by up to 31.90%.

Pre-training graph foundation models (GFMs) on text-attributed graphs (TAGs) is important for web-scale retrieval and recommendation, where graph entities are matched with textual descriptions. Existing CLIP-style graph-text aligners typically assume one-to-one correspondence: each node is pulled close only to its paired text, and all other pairs are treated as negatives. This overlooks the many-to-many relations common in real TAGs, where a node and its local neighborhood can be semantically related to multiple texts, and vice versa. Meanwhile, TAG supervision is often imperfect: noisy or weak node-text links introduce false-positive pairs, causing contrastive learning to align mismatched semantics. These limitations reveal a fundamental trade-off: leveraging expressive many-to-many signals increases semantic coverage but may propagate errors under noise, whereas strict one-to-one training is more conservative yet still suffers when mismatched pairs remain in the training set. Therefore, we propose ADAligner, a quality-aware graph–text alignment framework that adapts between expressive many-to-many and conservative one-to-one objectives based on estimated alignment reliability. ADAligner tracks batch-level reliability online and adjusts optimization accordingly—promoting soft, subgraph-level alignment when supervision is clean while emphasizing reliable one-to-one alignment by filtering low-confidence pairs under noise. We provide theoretical analysis showing that this closed-loop adaptation is stable and convergent. Experiments on nine TAG benchmarks show that, under 30% mismatched node-text supervision, ADAligner consistently improves cross-modal retrieval by 144.70% on average, zero-/few-shot node classification by 26.13%, and link prediction by 4.70% over the strongest multimodal baseline, demonstrating strong robustness to alignment noise across both unsupervised and transfer settings. Our code is available at https://github.com/karmaisacat-13/ADAligner.

Knowledge graphs (KGs) are essential in many AI applications but often suffer from incompleteness, limiting their utility. Many relations in KGs have only a few examples, making it challenging to train accurate models. Few-shot learning offers a promising direction by enabling KG completion with only a small number of training triplets. However, most existing approaches treat each relation independently and fail to leverage shared information across tasks. In this paper, we introduce TransNet, a transfer learning method for few-shot KG completion that captures task relationships and reuses knowledge from related tasks. TransNet further incorporates meta-learning to effectively handle unseen relations. Experiments on standard benchmarks demonstrate that TransNet achieves strong performance compared to prior methods. Code and data will be released upon acceptance.

This paper presents the Local Homophily Graph Neural Network (LH-GNN), a novel framework for Graph Anomaly Detection (GAD).Anomalous activities in graphs often exhibit a complex interplay of homophily and heterophily, with our analysis revealing that anomalous nodes typically display a higher degree of heterophily compared to normal nodes.Existing GNN-based methods start to incorporate heterophily modeling but fail to address two critical challenges: (1) the efficiency challenge, as traditional spectral decomposition based methods are computationally expensive, and (2) the local homophily estimation challenge, where prior knowledge of node-wise homophily ratios is often unavailable.To address these challenges, LH-GNN introduces a lightweight polynomial graph filter that dynamically adjusts to node-specific homophily ratios, enabling efficient representation learning for both normal and anomalous nodes through adaptable heterophilic and homophilic bases.This design achieves linear time complexity, significantly improving computational efficiency.Additionally, we propose an iterative prototype learning strategy to estimate local homophily values without requiring additional labels.This strategy leverages class prototypes and uncertainty measures to assign reliable pseudolabels, effectively capturing node-wise homophily.Together, these innovations enable LH-GNN to overcome the limitations of existing methods.Extensive experiments on four benchmark datasets demonstrate that LH-GNN outperforms state-of-the-art methods in both effectiveness and efficiency, achieving 4.4% improvements in detection accuracy and 11× computational speedup 1 .

Temporal graphs capture dynamic node relations via temporal edges, finding extensive utility in wide domains where time-varying patterns are crucial. Temporal Graph Neural Networks (TGNNs) have gained significant attention for their effectiveness in representing temporal graphs. However, TGNNs still face significant efficiency challenges in real-world low-resource settings. First, from a data-efficiency standpoint, training TGNNs requires sufficient temporal edges and data labels, which is problematic in practical scenarios with limited data collection and annotation. Second, from a resource-efficiency perspective, TGNN training and inference are computationally demanding due to complex encoding operations, especially on large-scale temporal graphs. Minimizing resource consumption while preserving effectiveness is essential. Inspired by these efficiency challenges, this tutorial systematically introduces state-of-the-art data-efficient and resource-efficient TGNNs, focusing on algorithms, frameworks, and tools, and discusses promising yet under-explored research directions in efficient temporal graph learning. This tutorial aims to benefit researchers and practitioners in data mining, machine learning, and artificial intelligence.

Temporal graphs, depicting time-evolving node connections through temporal edges, are extensively utilized in domains where temporal connection patterns are essential, such as recommender systems, financial networks, healthcare, and sensor networks. Despite recent advancements in temporal graph representation learning, performance degradation occurs with periodic collections of new temporal edges, owing to their dynamic nature and newly emerging information. This paper investigates online representation learning on temporal graphs, aiming for efficient updates of temporal models to sustain predictive performance during deployment. Unlike costly retraining or exclusive fine-tuning susceptible to catastrophic forgetting, our approach aims to distill information from previous model parameters and adapt it to newly gathered data. To this end, we propose TGOnline, an adaptive online meta-learning framework, tackling two key challenges. First, to distill valuable knowledge from complex temporal parameters, we establish an optimization objective that determines new parameters, either by leveraging global ones or by placing greater reliance on new data, where global parameters are meta-trained across various data collection periods to enhance temporal generalization. Second, to accelerate the online distillation process, we introduce an edge reduction mechanism that skips new edges lacking additional information and a node deduplication mechanism to prevent redundant computation within training batches on new data. Extensive experiments on four real-world temporal graphs demonstrate the effectiveness and efficiency of TGOnline for online representation learning, outperforming 18 state-of-the-art baselines. Notably, TGOnline not only outperforms the commonly utilized retraining strategy but also achieves a significant speedup of ~30x.

This paper investigates the few-shot temporal reasoning capability within the hyperbolic space. The goal is to forecast future events for newly emerging entities within temporal knowledge graphs (TKGs), leveraging only a limited set of initial observations. Hyperbolic space is advantageous for modeling emerging graph entities for two reasons: First, its geometric property of exponential expansion aligns with the rapid growth of new entities in real-world graphs; Second, it excels in capturing power-law patterns and hierarchical structures, well-suitable for new entities distributed at the peripheries of graph hierarchies and loosely connected with others through few links. We therefore propose a meta-learning framework, MetaHKG, to enable few-shot temporal reasoning within a hyperbolic space. Unlike prior hyperbolic learning works, MetaHKG addresses the challenges of effectively representing new entities in TKGs and adapting model parameters by incorporating novel hyperbolic time encodings and temporal attention networks that achieve translational invariance. We also introduce a meta hyperbolic optimization algorithm to enhance model adaptation by learning both global and entity-specific parameters through bi-level optimization. Comprehensive experiments conducted on three real-world temporal knowledge graphs demonstrate the superiority of MetaHKG over a diverse range of baselines, which achieves average 5.2% relative improvements. Compared to its Euclidean counterpart, MetaHKG operates in a lower-dimensional space but yields a more stable and efficient adaptability towards new entities.

This paper presents FreqMAE, a novel self-supervised learning framework that synergizes masked autoencoding (MAE) with physics-informed insights to capture feature patterns in multi-modal IoT sensor data. FreqMAE enhances latent space representation of sensor data, reducing reliance on data labeling and improving accuracy for AI tasks. Differing from data augmentation-based methods like contrastive learning, FreqMAE's approach eliminates the need for handcrafted transformations. Adapting MAE for IoT sensing signals, we present three contributions from frequency domain insights: First, a Temporal-Shifting Transformer (TS-T) encoder that enables temporal interactions while distinguishing different frequency bands; Second, a factorized multi-modal fusion mechanism for leveraging cross-modal correlations and preserving unique modality features; Third, a hierarchically weighted loss function that emphasizes important frequency components and high Signal-to-Noise Ratio (SNR) samples. Comprehensive evaluations on two sensing applications validate FreqMAE's proficiency in reducing labeling needs and enhancing resilience against domain shifts.

This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones. The cross-lingual distillation ability across TKGs becomes increasingly crucial, in light of the unsatisfying performance of existing reasoning methods on those severely incomplete TKGs, especially in low-resource languages. However, it poses tremendous challenges in two aspects. First, the cross-lingual alignments, which serve as bridges for knowledge transfer, are usually too scarce to transfer sufficient knowledge between two TKGs. Second, temporal knowledge discrepancy of the aligned entities, especially when alignments are unreliable, can mislead the knowledge distillation process. We correspondingly propose a mutually-paced knowledge distillation model MP-KD, where a teacher network trained on a source TKG can guide the training of a student network on target TKGs with an alignment module. Concretely, to deal with the scarcity issue, MP-KD generates pseudo alignments between TKGs based on the temporal information extracted by our representation module. To maximize the efficacy of knowledge transfer and control the noise caused by the temporal knowledge discrepancy, we enhance MP-KD with a temporal cross-lingual attention mechanism to dynamically estimate the alignment strength. The two procedures are mutually paced along with model training. Extensive experiments on twelve cross-lingual TKG transfer tasks in the EventKG benchmark demonstrate the effectiveness of the proposed MP-KD method.

Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in information loss. To address the problem, this paper treats vertices and hyperedges equally and proposes a new hypergraph expansion named the line expansion(LE) for hypergraphs learning. The new expansion bijectively induces a homogeneous structure from the hypergraph by modeling vertex-hyperedge pairs. Our proposal essentially reduces the hypergraph to a simple graph, which enables the existing graph learning algorithms to work seamlessly with the higher-order structure. We further prove that our line expansion is a unifying framework over various hypergraph expansions. We evaluate the proposed LE on five hypergraph datasets in terms of the hypergraph node classification task. The results show that our method could achieve at least 2% accuracy improvement over the best baseline consistently.

This paper develops a novel unsupervised algorithm for belief representation learning in polarized networks that (i) uncovers the latent dimensions of the underlying belief space and (ii) jointly embeds users and content items (that they interact with) into that space in a manner that facilitates a number of downstream tasks, such as stance detection, stance prediction, and ideology mapping. Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e.g., posts that represent user views) into an appropriate disentangled latent space. To better disentangle latent variables in that space, we develop a total correlation regularization module, a Proportional-Integral (PI) control module, and adopt rectified Gaussian distribution to ensure the orthogonality. The latent representation of users and content can then be used to quantify their ideological leaning and detect/predict their stances on issues. We evaluate the performance of the proposed InfoVGAE on three real-world datasets, of which two are collected from Twitter and one from U.S. Congress voting records. The evaluation results show that our model outperforms state-of-the-art unsupervised models by reducing 10.5% user clustering errors and achieving 12.1% higher F1 scores for stance separation of content items. In addition, InfoVGAE produces a comparable result with supervised models. We also discuss its performance on stance prediction and user ranking within ideological groups.

With the increasing demands on e-commerce platforms, numerous user action history is emerging. Those enriched action records are vital to understand users’ interests and intents. Recently, prior works for user behavior prediction mainly focus on the interactions with product-side information. However, the interactions with search queries, which usually act as a bridge between users and products, are still under investigated. In this paper, we explore a new problem named temporal event forecasting, a generalized user behavior prediction task in a unified query product evolutionary graph, to embrace both query and product recommendation in a temporal manner. To fulfill this setting, there involves two challenges: (1) the action data for most users is scarce; (2) user preferences are dynamically evolving and shifting over time. To tackle those issues, we propose a novel Retrieval-Enhanced Temporal Event (RETE) forecasting framework. Unlike existing methods that enhance user representations via roughly absorbing information from connected entities in the whole graph, RETE efficiently and dynamically retrieves relevant entities centrally on each user as high-quality subgraphs, preventing the noise propagation from the densely evolutionary graph structures that incorporate abundant search queries. And meanwhile, RETE autoregressively accumulates retrieval-enhanced user representations from each time step, to capture evolutionary patterns for joint query and product prediction. Empirically, extensive experiments on both the public benchmark and four real-world industrial datasets demonstrate the effectiveness of the proposed RETE method.

This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction on social media. Given the initial content and a sequence of forwarding users, DyDiff-VAE aims to estimate the propagation likelihood for other potential users and predict the corresponding user rankings. Inferring user interests from diffusion data lies the foundation of diffusion prediction, because users often forward the information in which they are interested or the information from those who share similar interests. Their interests also evolve over time as the result of the dynamic social influence from neighbors and the time-sensitive information gained inside/outside the social media. Existing works fail to model users' intrinsic interests from the diffusion data and assume user interests remain static along the time. DyDiff-VAE advances the state of the art in two directions: (i) We propose a dynamic encoder to infer the evolution of user interests from observed diffusion data. (ii) We propose a dual attentive decoder to estimate the propagation likelihood by integrating information from both the initial cascade content and the forwarding user sequence. Extensive experiments on four real-world datasets from Twitter and Youtube demonstrate the advantages of the proposed model; we show that it achieves 43.3%relative gains over the best baseline on average. Moreover, it has the lowest run-time compared with recurrent neural network based models.

Much work on social media opinion polarization focuses on a flat categorization of stances (or orthogonal beliefs) of different communities from media traces. We extend in this work in two important respects. First, we detect not only points of disagreement between communities, but also points of agreement. In other words, we estimate community beliefs in the presence of overlap. Second, in lieu of flat categorization, we consider hierarchical belief estimation, where communities might be hierarchically divided. For example, two opposing parties might disagree on core issues, but within a party, despite agreement on fundamentals, disagreement might occur on further details. We call the resulting combined problem a hierarchical overlapping belief estimation problem. To solve it, this paper develops a new class of unsupervised Non-negative Matrix Factorization (NMF) algorithms, we call Belief Structured Matrix Factorization (BSMF). Our proposed unsupervised algorithm captures both the latent belief intersections and dissimilarities, as well as hierarchical structure. We discuss properties of the algorithm and evaluate it on both synthetic and real-world datasets. In the synthetic dataset, our model reduces error by 40%. In real Twitter traces, it improves accuracy by around 10%. The model also achieves 96.08% self-consistency in a sanity check.

Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing. In this paper, we present AceKG, a new large-scale KG in academic domain. AceKG not only provides clean academic information, but also offers a large-scale benchmark dataset for researchers to conduct challenging data mining projects including link prediction, community detection and scholar classification. Specifically, AceKG describes 3.13 billion triples of academic facts based on a consistent ontology, including necessary properties of papers, authors, fields of study, venues and institutes, as well as the relations among them. To enrich the proposed knowledge graph, we also perform entity alignment with existing databases and rule-based inference. Based on AceKG, we conduct experiments of three typical academic data mining tasks and evaluate several state-of-the-art knowledge embedding and network representation learning approaches on the benchmark datasets built from AceKG. Finally, we discuss promising research directions that benefit from AceKG.