Online social networks have become an integral part of modern society, profoundly influencing how individuals form and exchange opinions across diverse domains ranging from politics to public health. The Friedkin-Johnsen model serves as a foundational framework for modeling opinion formation dynamics in such networks. In this paper, we address the computational task of efficiently determining the equilibrium opinion vector and associated metrics including polarization and disagreement, applicable to both directed and undirected social networks. We propose a deterministic local algorithm with relative error guarantees, scaling to networks exceeding ten million nodes. Further acceleration is achieved through integration with successive over-relaxation techniques, where a relaxation factor optimizes convergence rates. Extensive experiments on diverse real-world networks validate the practical effectiveness of our approaches, demonstrating significant improvements in computational efficiency and scalability compared to conventional methods.
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Point-of-interest (POI) recommendation systems aim to predict the next destinations of user based on their preferences and historical check-ins. Existing generative POI recommendation methods usually employ random numeric IDs for POIs, limiting the ability to model semantic relationships between similar locations. In this paper, we propose Generative Next POI Recommendation with Semantic ID (GNPR-SID), an LLM-based POI recommendation model with a novel semantic POI ID (SID) representation method that enhances the semantic understanding of POI modeling. There are two key components in our GNPR-SID: (1) a Semantic ID Construction module that generates semantically rich POI IDs based on semantic and collaborative features, and (2) a Generative POI Recommendation module that fine-tunes LLMs to predict the next POI using these semantic IDs. By incorporating user interaction patterns and POI semantic features into the semantic ID generation, our method improves the recommendation accuracy and generalization of the model. To construct semantically related SIDs, we propose a POI quantization method based on residual quantized variational autoencoder, which maps POIs into a discrete semantic space. We also propose a diversity loss to ensure that SIDs are uniformly distributed across the semantic space. Extensive experiments on three benchmark datasets demonstrate that GNPR-SID substantially outperforms state-of-the-art methods, achieving up to 16% improvement in recommendation accuracy.
Discriminative recommendation tasks, such as CTR (click-through rate) and CVR (conversion rate) prediction, play critical roles in the ranking stage of large-scale industrial recommender systems. However, training a discriminative model encounters a significant overfitting issue induced by data sparsity. Moreover, this overfitting issue worsens with larger models, causing them to underperform smaller ones. To address the overfitting issue and enhance model scalability, we propose a framework named GPSD (Generative Pretraining for Scalable Discriminative Recommendation), drawing inspiration from generative training, which exhibits no evident signs of overfitting. GPSD leverages the parameters learned from a pretrained generative model to initialize a discriminative model, and subsequently applies a sparse parameter freezing strategy. Extensive experiments conducted on both industrial-scale and publicly available datasets demonstrate the superior performance of GPSD. Moreover, it delivers remarkable improvements in online A/B tests. GPSD offers two primary advantages: 1) it substantially narrows the generalization gap in model training, resulting in better test performance; and 2) it leverages the scalability of Transformers, delivering consistent performance gains as models are scaled up. Specifically, we observe consistent performance improvements as the model dense parameters scale from 13K to 0.3B, closely adhering to power laws. These findings pave the way for unifying the architectures of recommendation models and language models, enabling the direct application of techniques well-established in large language models to recommendation models.
Graph Few-Shot Node Classification (GFSNC) has emerged as a promising approach to address the challenge of learning with limited labeled data in graph-structured networks. Despite the success of Graph Neural Networks (GNNs) in node classification tasks, their performance heavily depends on the availability of abundant labeled data, which is often impractical in real-world scenarios. To tackle this issue, GFSNC adopts the episodic meta-learning paradigm, where models are trained on a series of meta-tasks. However, existing methods face two critical limitations: (i) they focus on local distributions within individual meta-tasks, neglecting the global data distribution, and (ii) they optimize models to minimize intra-class distances without adequately addressing inter-class separability, leading to suboptimal performance. This paper presents TaskNS, a novel GFSNC framework that introduces task-negative samples into meta-training tasks to address these limitations. By incorporating samples from classes outside the current meta-task, our framework enables the model to gradually learn the global distribution of the graph data. Additionally, we design a novel loss function that enhances the model's ability to distinguish between different classes of query samples. This loss function not only ensures high intra-class compactness but also maximizes the inter-class separation by leveraging task-negative samples. To further enhance the quality of task-negative samples, we propose an h-hop-neighbors-based sampling method that leverages the topological structure of a graph. It selects task-negative samples that are structurally close to query samples, ensuring that they are informative and challenging for the model to classify. Extensive experiments on four benchmark datasets demonstrate the effectiveness of TaskNS, achieving average improvements of 4.6% in accuracy (ACC) and 4.9% in F1-score over state-of-the-art methods.
Hyperparameter optimization is crucial for maximizing machine learning model performance, yet most existing algorithms are designed for batch or offline scenarios and assume static data distributions. Such assumptions fall short in data stream settings, where models must adapt to evolving inputs in real time. To address these limitations, we propose the Bayesian Stream Tuner (BST), a novel framework for online hyperparameter optimization in non-stationary data streams. BST maintains a dynamic set of candidate hyperparameter configurations and periodically refines them using an incremental Bayesian model, which estimates configuration performance based on recent data statistics and hyperparameter values. This systematic exploration and refinement strategy allows BST to detect and respond to concept drift by resetting its adaptation mechanisms whenever necessary, ensuring strong performance under changing distributions. Our theoretical analysis establishes sublinear regret bounds for BST in dynamic environments, and extensive experiments on classification and regression tasks demonstrate that BST consistently outperforms state-of-the-art online hyperparameter optimization methods in both predictive accuracy and adaptability, making it a powerful solution for real-time hyperparameter tuning in evolving data streams.
Graph Neural Networks (Gnns) have achieved remarkable success in various downstream tasks, such as node classification and link prediction. Yet, efficiently deploying Gnns remains a challenge due to their computational complexity. Graph knowledge distillation aims to address this by transferring task-specific structural knowledge from teacher Gnns to lightweight student Gnns or Multi-Layer Perceptrons (MLPs). Despite its promise, existing distillation approaches suffer from several limitations: (i) they require extensive task-specific supervision(ii) they must be retrained separately for each downstream task, and (iii) they often struggle in heterophilous settings. To overcome these challenges, we propose TAG2M, a Task-Agnostic Gnn-to-MLP distillation framework designed for efficient and accurate few-shot inference. TAG2M introduces several novel strategies, including a self-supervised contrastive loss that captures topological information solely from node attributes. Additionally, it leverages Lipschitz embeddings to encode positional information with provable distortion bounds, ensuring robust representation learning. To further enhance adaptability for few-shot inference, TAG2M incorporates a learnable prompt head, which facilitates rapid task adaptation even in label-scarce settings. Unlike prior methods, TAG2M generalizes well across both homophilous and heterophilous datasets while delivering a significant computational advantage, achieving up to a 20X -200X speed-up. Extensive evaluations on 11 public datasets demonstrate its superior accuracy across diverse tasks, including node classification, link prediction, and node regression, outperforming state-of-the-art approaches.
Knowledge-to-Jailbreak: Investigating Knowledge-driven Jailbreaking Attacks for Large Language Models
PDF ↗Large language models (LLMs) have been increasingly applied to various domains, which triggers increasing concerns about LLMs' safety on specialized domains, e.g. medicine. Despite prior explorations on general jailbreaking attacks, there are two challenges for applying existing attacks on testing the domain-specific safety of LLMs: (1) Lack of professional knowledge-driven attacks(2) Insufficient coverage of domain knowledge. To bridge this gap, we propose a new task, knowledge-to-jailbreak, which aims to generate jailbreaking attacks from domain knowledge, requiring both attack effectiveness and knowledge relevance. We collect a large-scale dataset with 12,974 knowledge-jailbreak pairs and fine-tune a large language model as jailbreak-generator, to produce domain knowledge-specific jailbreaks. Experiments on 13 domains and 8 target LLMs demonstrate the effectiveness of jailbreak-generator in generating jailbreaks that are both threatening to the target LLMs and relevant to the given knowledge. We also apply our method to an out-of-domain knowledge base, showing that jailbreak-generator can generate jailbreaks that are comparable in harmfulness to those crafted by human experts. Data and code are available at: https://github.com/THU-KEG/Knowledge-to-Jailbreak/.
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification
PDF ↗Contrastive learning has emerged as a competent approach for unsupervised representation learning. However, the design of an optimal augmentation strategy, although crucial for contrastive learning, is less explored for time series classification tasks. Existing predefined time-domain augmentation methods are primarily adopted from vision and are not specific to time series data. Consequently, this cross-modality incompatibility may distort the semantically relevant information of time series by introducing mismatched patterns into the data. To address this limitation, we present a novel perspective from the frequency domain and identify three advantages for downstream classification: 1) the frequency component naturally encodes global features, 2) the orthogonal nature of the Fourier basis allows easier isolation and independent modifications of critical and unimportant information, and 3) a compact set of frequency components can preserve semantic integrity. To fully utilize the three properties, we propose the lightweight yet effective Frequency-Refined Augmentation (FreRA) tailored for time series contrastive learning on classification tasks, which can be seamlessly integrated with contrastive learning frameworks in a plug-and-play manner. Specifically, FreRA automatically separates critical and unimportant frequency components. Accordingly, we propose semantic-aware Identity Modification and semantic-agnostic Self-adaptive Modification to protect semantically relevant information in the critical frequency components and infuse variance into the unimportant ones respectively. Theoretically, we prove that FreRA generates semantic-preserving views. Empirically, we conduct extensive experiments on two benchmark datasets, including UCR and UEA archives, as well as five large-scale datasets on diverse applications. FreRA consistently outperforms ten leading baselines on time series classification, anomaly detection, and transfer learning tasks, demonstrating superior capabilities in contrastive representation learning and generalization in transfer learning scenarios across diverse datasets. The code is available at https://github.com/Tian0426/FreRA.
Non-fungible tokens (NFTs) have emerged as a transformative innovation in art and technology, relying heavily on social networks for promotion and revenue generation. The value of NFTs is profoundly influenced by their scarcity, rarity, and unique breeding mechanisms, which present novel challenges for viral marketing strategies. In this paper, we introduce a new research problem of NFT Revenue Maximization (NRM), which focuses on maximizing revenue from the perspective of NFT marketplaces by optimally selecting users for viral marketing campaigns (NFT airdrops) and determining the ideal quantities of NFTs to release. We prove the hardness of NRM and propose an approximation algorithm named Quantity and Offspring-Oriented Airdrops (QOOA). Our algorithm leverages the concepts of Scarcity-Conscious Revenue and Valuation-based Quantity Inequality to prune suboptimal airdrops and quantities at an early stage. To further enhance revenue through NFT breeding, QOOA identifies and incentivizes Rare Trait Collectors to acquire multiple NFTs with rare traits, facilitating the breeding of high-value offspring. Experimental results demonstrate that QOOA significantly outperforms baselines, achieving up to 3.8 times higher revenue in large-scale social networks.
Alpha mining, a critical component in quantitative investment, focuses on discovering predictive signals for future asset returns in increasingly complex financial markets. However, the pervasive issue of alpha decay-where factors lose their predictive power over time-poses a significant challenge for alpha mining. Traditional methods such as genetic programming are prone to rapid alpha decay, primarily due to their susceptibility to overfitting. At the same time, approaches driven by Large Language Models (LLMs), despite their promise, often fail to impose regularization against factor homogenization-resulting in crowded signals and accelerated decay. To address this challenge, we propose AlphaAgent, an autonomous framework that effectively integrates LLM-driven agents with ad hoc regularization for mining decay-resistant alpha factors. AlphaAgent employs three key mechanisms: (i) originality enforcement through a similarity measure based on abstract syntax trees (ASTs) against existing alphas(ii) hypothesis-factor alignment via LLM-evaluated semantic consistency between market hypotheses and generated factors, and (iii) complexity control via AST-based structural constraints, preventing over-engineered constructions that are prone to overfitting. These mechanisms collectively guide the alpha generation process to balance originality, financial rationale, and adaptability to evolving market conditions, mitigating the risk of alpha decay. Extensive evaluations show that AlphaAgent outperforms traditional and LLM-based methods in mitigating alpha decay across bull and bear markets, consistently delivering significant alpha in Chinese CSI 500 and U.S. S&P 500 markets over the past four years. Notably, AlphaAgent showcases remarkable resistance to alpha decay, elevating the potential for yielding powerful factors.
A set of categorical items is a common data structure used in various applications, such as customer orders in online shopping. With the rise of big data and e-commerce, there is an increasing need to store such a large number of set data efficiently. This paper presents a novel lossless compression method that uses binary trees for set compression. The compressed code is generated by performing a depth-first search for items in the set on the binary tree. We prove that finding the optimal tree for minimizing the total code length for a given collection of sets is NP-hard, and the problem even remains NP-hard when we only need to assign items to the leaf nodes of a given tree. A heuristic algorithm is then proposed for building trees. To further improve the compression efficiency, we incorporate the entropy coding method into the coding process. Moreover, we reveal the underlying probabilistic expression of the binary tree model and present the conditional independence structure of the model. To validate the effectiveness of the proposed method, empirical studies are conducted on two real-world online e-commerce datasets. The results show that the proposed coding method significantly reduces the storage space required for set data and the tree structure for compressing sets can help identify conditional independent items.
Popularity bias occurs when popular items are recommended far more frequently than they should be, negatively impacting both user experience and recommendation accuracy. Existing debiasing methods mitigate popularity bias often uniformly across all users and only partially consider the time evolution of users or items. However, users have different levels of preference for item popularity, and this preference is evolving over time. To address these issues, we propose a novel method called CausalEPP (Causal Intervention on Evolving Personal Popularity) for taming recommendation bias, which accounts for the evolving personal popularity of users. Specifically, we first introduce a metric called Evolving Personal Popularity to quantify each user's preference for popular items. Then, we design a causal graph that integrates evolving personal popularity into the conformity effect, and apply deconfounded training to mitigate the popularity bias of the causal graph. During inference, we consider the evolution consistency between users and items to achieve a better recommendation. Empirical studies demonstrate that CausalEPP outperforms baseline methods in reducing popularity bias while improving recommendation accuracy.
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery
PDF ↗Inferring causal relationships between variable pairs is crucial for understanding multivariate interactions in complex systems. Knowledge-based causal discovery -which involves inferring causal relationships by reasoning over the metadata of variables (e.g., names or textual context)-offers a compelling alternative to traditional methods that rely on observational data. However, existing methods using Large Language Models (LLMs) often produce unstable and inconsistent results, compromising their reliability for causal inference. To address this, we introduce a novel approach that integrates Knowledge Graphs (KGs) with LLMs to enhance knowledge-based causal discovery. Our approach identifies informative metapath -based subgraphs within KGs and further refines the selection of these subgraphs using Learning-to-Rank-based models. The top-ranked subgraphs are then incorporated into zero-shot prompts, improving the effectiveness of LLMs in inferring the causal relationship. Extensive experiments on biomedical and open-domain datasets demonstrate that our method outperforms most baselines by up to 44.4 points in F1 scores, evaluated across diverse LLMs and KGs. Our code and datasets are available on GitHub.. https://github.com/susantiyuni/path-to-causality
Topological tools have become popular in improving and evaluating the performance of generative models by exploring the connection between their representation power and topological properties. This has led to the development of various measures that can assess the diversity and quality of generated data. However, existing methods are impractical in higher dimensions and large-scale datasets/models. To address this, we propose a scalable framework based on persistent entropy. We first establish a theoretical relation between the homological complexity of the underlying topology and the persistent entropy. We then empirically study the topological transformation during training of the generated data manifold using cubical homology. The proposed method is domain & model-agnostic and scales well for various neural architectures at different depths.
Next location prediction is a critical task in human mobility modeling, with broad applications in personalized recommendation, urban planning, and location-based services. Recently, researchers have used prompt-based large language models (LLMs) to improve next location prediction with pre-trained knowledge. However, they face inherent challenges in bridging the gap between textual prompts for semantic contextual understanding and human mobility data for transition pattern modeling. In this paper, we introduce SILO, a framework designed for Semantic Integration in LOcation prediction via LLMs. We first construct a hybrid semantic space that seamlessly integrates ID-based embeddings, text-derived semantics, and auxiliary contextual information, enabling comprehensive modeling of sequential mobility patterns alongside contextual nuances. We then propose user-centric prompts that specify the prediction task for LLMs while embedding user context within a special token. Further, we utilize LLMs as the prediction backbone to process both user-specific prompts and hybrid ID-context embeddings of location sequences. To enhance predictive performance, we finally introduce a dual-logits strategy, combining sequential transition logits with user profile-guided semantic preference logits. Extensive experiments on two large-scale real-world mobility datasets demonstrate that SILO significantly outperforms state-of-the-art baselines, validating its effectiveness in modeling complex mobility patterns through semantic integration using LLMs.
Generalizable Graph Prompt Learning Framework with Model-level Prompt Injection and Two-Stage Prompt Tuning
PDF ↗Graph prompt learning represents a novel paradigm aimed at enhancing the performance of graph learning models on a variety of downstream tasks by providing specific graph prompts. Despite its promise, current graph prompt learning methods are limited by the following limitations. On the one hand, existing methods often rely on manually selected graph information or simple learnable vectors, which can introduce human biases and lack expressiveness. These methods also fall short in guiding models to induce historical prior knowledge and improve generalization. Furthermore, the direct end-to-end tuning strategy of prompts lacks a necessary gentle transition, which impacts model stability and generalization. To overcome these limitations, we introduce the generalizable graph prompt learning framework (GGPL), which incorporates model-level prompt injection and a two-stage prompt tuning strategy. GGPL focuses on encoding subgraph structures and attributes during pre-training and uses SimGRACE to predict subgraph similarities, enhancing the base model's generalization. The model-level prompt injection module, with its prompt embedding backbone and self-prompt generation, seamlessly integrates invariant knowledge. Our two-stage tuning strategy, including transition and task-specific tuning, ensures better guidance and stability. By designing learnable prompt tokens and fine-tuning them with task-specific information, GGPL enables the model to generalize more robustly to downstream tasks. We conduct extensive experiments on six benchmark datasets to verify the model's effectiveness.
Combining Denoised Neural Network and Genetic Symbolic Regression for Memory Behavior Modeling via Dynamic Asynchronous Optimization
PDF ↗Memory behavior modeling is a key topic in cognitive psychology and education. Traditional approaches use experimental data to build memory equations, but these models often lack precision and are debated in form. Recently, data-driven methods have improved predictive accuracy but struggle with interpretability, limiting cognitive insights. Although knowledge-informed neural networks have succeeded in fields like physics, their use in behavior modeling is still limited. This paper proposes a Self-evolving Psychology-informed Neural Network (SPsyINN), which leverages classical memory equations as knowledge modules to constrain neural network training. To address challenges such as the difficulty in quantifying descriptors and the limited interpretability of classical memory equations, a genetic symbolic regression algorithm is introduced to conduct evolutionary searches for more optimal expressions based on classical memory equations, enabling the mutual progress of the knowledge module and the neural network module. Specifically, the proposed approach combines genetic symbolic regression and neural networks in a parallel training framework, with a dynamic joint optimization loss function ensuring effective knowledge alignment between the two modules. Then, for addressing the training efficiency differences arising from the distinct optimization methods and computational hardware requirements of genetic algorithms and neural networks, an asynchronous interaction mechanism mediated by proxy data is developed to facilitate effective communication between modules and improve optimization efficiency. Finally, a denoising module is integrated into the neural network to enhance robustness against data noise and improve generalization performance. Experimental results on five large-scale real-world memory behavior demonstrate that SPsyINN outperforms state-of-the-art methods in predictive accuracy. Ablation studies confirm the model's co-evolution capability, improving accuracy while discovering more interpretable memory equations, showing its potential for psychological research. Our code is released at: https://github.com/JiaqiDijon/SPsyINN
PMKLC: Parallel Multi-Knowledge Learning-based Lossless Compression for Large-Scale Genomics Database
PDF ↗Learning-based lossless compressors play a crucial role in large-scale genomic database backup, storage, transmission, and management. However, their 1) inadequate compression ratio, 2) low compression & decompression throughput, and 3) poor compression robustness limit their widespread adoption and application in both industry and academia. To solve those challenges, we propose a novel Parallel Multi-Knowledge Learning-based Compressor (PMKLC) with four crucial designs: 1) We propose an automated multi-knowledge learning-based compression framework as compressors' backbone to enhance compression ratio and robustness; 2) we design a GPU-accelerated (s,k)-mer encoder to optimize compression throughput and computing resource usage; 3) we introduce data block partitioning and Step-wise Model Passing (SMP) mechanisms for parallel acceleration; 4) We design two compression modes PMKLC-S and PMKLC-M to meet the complex application scenarios, where the former runs on a resource-constrained single GPU and the latter is multi-GPU accelerated. We benchmark PMKLC-S/M and 14 baselines (7 traditional and 7 leaning-based) on 15 real-world datasets with different species and data sizes. Compared to baselines on the testing datasets, PMKLC-S/M achieve the average compression ratio improvement up to 73.609% and 73.480%, the average throughput improvement up to 3.036X and 10.710X, respectively. Besides, PMKLC-S/M also achieve the best robustness and competitive memory cost, indicating its greater stability against datasets with different probability distribution perturbations, and its strong ability to run on memory-constrained devices. Overall, PMKLC is a balanced compression solution that optimizes compression ratio, throughput, robustness, and resource consumption. PMKLC and linkages of datasets are available at https://github.com/dingyanfeng/PMKLC.
Online recruitment platforms have reshaped job-seeking and recruiting processes, driving increased demand for applications that enhance person-job matching. Traditional methods generally rely on analyzing textual data from resumes and job descriptions, limiting the dynamic, interactive aspects crucial to effective recruitment. Recent advances in Large Language Models (LLMs) have revealed remarkable potential in simulating adaptive, role-based dialogues, making them well-suited for recruitment scenarios. In this paper, we propose MockLLM, a novel framework to generate and evaluate mock interview interactions. The system consists of two key components: mock interview generation and two-sided evaluation in handshake protocol. By simulating both interviewer and candidate roles, MockLLM enables consistent and collaborative interactions for real-time and two-sided matching. To further improve the matching quality, MockLLM further incorporates reflection memory generation and dynamic strategy modification, refining behaviors based on previous experience. We evaluate MockLLM on real-world data Boss Zhipin, a major Chinese recruitment platform. The experimental results indicate that MockLLM outperforms existing methods in matching accuracy, scalability, and adaptability across job domains, highlighting its potential to advance candidate assessment and online recruitment.
The increasing availability of urban data offers new opportunities for learning region representations, which can be used as input to machine learning models for downstream tasks such as check-in or crime prediction. While existing solutions have produced promising results, an issue is their fixed formation of regions and fixed input region features, which may not suit the needs of different downstream tasks. To address this limitation, we propose a model named FlexiReg for urban region representation learning that is flexible with both the formation of urban regions and the input region features. FlexiReg is based on a spatial grid partitioning over the spatial area of interest. It learns representations for the grid cells, leveraging publicly accessible data, including POI, land use, satellite imagery, and street view imagery. We propose adaptive aggregation to fuse the cell representations and prompt learning techniques to tailor the representations towards different tasks, addressing the needs of varying formations of urban regions and downstream tasks. Extensive experiments on five real-world datasets demonstrate that FlexiReg outperforms state-of-the-art models by up to 202% in term of the accuracy of four diverse downstream tasks using the produced urban region representations.