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Yahong Lian, Chunyao Song, Tingjian Ge

Personalized recommendations are integrated into daily life, but providers may want certain items to become more appealing over time through user interactions, yet this issue is often overlooked. The existing works are often based on the assumption that users will passively accept all intermediate sequences or not explore intention modeling in the targeted nudging process. Both of these factors result in suboptimal performance in the proactive recommendation. In this paper, we propose a novel intention-based targeted multi-round proactive recommendation method, dubbed ITMPRec. We first select target items using a pre-match strategy. Then, we employ a multi-round nudging recommendation method, incorporating a module to quantify users' intention-level evolution, helping choose suitable intermediate items. Additionally, we model users' sensitivity to changes caused by these items. Lastly, we propose an LLM agent as a pluggable component to simulate user feedback, offering an alternative to traditional click models by leveraging the agent's external knowledge and reasoning capabilities. Through extensive experiments on four public datasets, we demonstrate the superiority of ITMPRec compared to eight baseline models.

Wei Li 0308, Jiawen Deng 0006, Jiali You 0002, Yuanyuan He, Yan Zhuang 0002, Fuji Ren

Social bots are becoming increasingly common in social networks, and their activities affect the security and authenticity of social media platforms. Current state-of-the-art social bot detection methods leverage multimodal approaches that analyze various modalities, such as user metadata, text, and social network relationships. However, these methods may not always extract additional dimensions of semantic feature information that could offer a deeper understanding of users' social patterns. To address this issue, we propose ETS-MM, a multimodal detection framework designed to augment multidimensional information from text and extract the semantic feature representation of user text information. We first analyze the user's tweeting behavior based on topic preference and emotion tendency, integrating them into the textual data. Then, we try to extract enhanced semantic representations that reveal the latent relationship between tweeting behavior and tweet content while identifying potential contextual associations and emotional changes. Additionally, to capture the complex interaction between users, we integrate the user's multimodal information, including metadata, textual features, enhanced semantic features, and social network relationships to propagate and aggregate information across various modalities. Experimental results demonstrate that ETS-MM significantly outperforms existing methods across two widely used social bot detection benchmark datasets, validating its effectiveness and superiority.

Xinyao Xu 0002, Ziyu Mao, Jianzhong Su, Xingwei Lin, David A. Basin, Jun Sun 0001, Jingyi Wang 0004

The rapid growth of decentralized applications, while revolutionizing financial transactions, has created an attractive target for malicious attacks. Existing approaches to detecting attacks often rely on predefined rules or simplistic and overly-specialized models, which lack the flexibility to handle the wide spectrum of diverse and dynamically changing attack types. To address this challenge, we present a general and extensible framework, MoE (Monitoring Ethereum), that leverages runtime verification to detect a wide range of attacks on Ethereum. MoE features an expressive attack modeling language, based on Metric First-order Temporal Logic (MFOTL), that can formalize a wide range of attacks. We integrate a novel semantic lifting approach that extracts system behaviors relevant for various attacks, which can be analyzed using the monitoring tool MonPoly. Furthermore, we also equip MoE with quantitative capabilities to evaluate the similarity between a transaction and an attack formula to enhance its performance in identifying attacks, including near-miss attacks. We carry out extensive experiments with MoE on a labeled benchmark and a large-scale dataset containing over one million transactions. On the labeled benchmark, MoE successfully detects 92.0% attacks and achieves a 45.0% higher recall rate than competing state-of-the-art tool. MoE finds 3,319 attacks with 95.4% precision on the large dataset. Furthermore, MoE uses quantitative analysis to uncover 8% additional attacks. Finally, the average time for monitoring a transaction is less than 23 ms, positioning MoE as a promising practical solution for real-time attack detection for Ethereum.

Bao Wen, Jingjing Gu, Hao Han, Pengfei Yu 0002, Yang Liu 0390

WebAssembly (Wasm) is a universal low-level bytecode designed to build modern web systems. Recent studies have shown that technologies such as voltage scaling and RowHammer attacks are expected to increase the likelihood of bit flips, which may cause unacceptable or catastrophic system failures. This raises concerns about the impact of bit flips on Wasm programs, which run as instructions in web systems, and it is an undeveloped topic since the features of Wasm differ from traditional programs. In this paper, we propose a novel paradigm, namely IVPSEG, to understand the error propagation of bit flips within Wasm programs. Specifically, we first use Large Language Models (LLMs) to automatically extract instruction embeddings containing semantic knowledge of each instruction's context. Then, we exploit these embeddings and program structure (control execution and data transfer) to construct a semantic enhanced code property graph, which implicates the potential path of error propagation. Based on this graph, we utilize graph neural networks and attention diffusion to optimize instruction embeddings by capturing different error propagation patterns for instruction vulnerability prediction. In particular, we build a Wasm compilation and fault generation system to simulate bit flips at Wasm runtime. Our experimental results with 14 benchmark programs and test cases show IVPSEG outperforms the state-of-the-art methods in terms of accuracy (average 13.06%ͽ ), F1-score (average 14.93%↑), and model robustness.

Siddhant Kharbanda, Devaansh Gupta, Gururaj K, Pankaj Malhotra, Amit Singh 0003, Cho-Jui Hsieh, Rohit Babbar

Extreme Multi-label Classification (XMC) involves predicting a subset of relevant labels from an extremely large label space, given an input query and labels with textual features. Models developed for this problem have conventionally made use of dual encoder (DE) to embed the queries and label texts and one-vs-all (OvA) classifiers to rerank the shortlisted labels by the DE. While such methods have shown empirical success, a major drawback is their computational cost, often requiring upto 16 GPUs to train on the largest public dataset. Such a high cost is a consequence of calculating the loss over the entire label space. While shortlisting strategies have been proposed for classifiers, we aim to study such methods for the DE framework. In this work, we develop UniDEC, a loss-independent, end-to-end trainable framework which trains the DE and classifier together in a unified manner with a multi-class loss, while reducing the computational cost by 4--16x. This is done via the proposed pick-some-label (PSL) reduction, which aims to compute the loss on only a subset of positive and negative labels. These labels are carefully chosen in-batch so as to maximise their supervisory signals. Not only does the proposed framework achieve state-of-the-art results on datasets with labels in the order of millions, it is also computationally and resource efficient in achieving this performance on a single GPU. Code is made available at https://github.com/the-catalyst/UniDEC.

Hui Wang 0074, Xin Wang 0030, Jiake Ge, Lei Liang 0002, Peng Yi

In real-world tasks like data management and Web search, index operations often exhibit strong skewness, unlike standard benchmarks with uniform data distribution. While learned indexes improve query and update efficiency, they typically fail to address the skewed workload access, often prioritizing a single performance metric at the cost of overall index effectiveness. Additionally, the full reliance on learned models can increase vulnerability to attacks, compromising system stability. To address these challenges, we propose ShapeShifter, an adaptive evolutionary structure based on traditional indexes, capable of dynamically adjusting node structures according to the workload. ShapeShifter introduces a node evolution strategy with workload-skew-aware policies to adaptively adjust and optimize the partial index structure, leveraging a hybrid mechanism that combines traditional and learned structures for robust performance and optimal time-space tradeoff under skewed workloads and extreme data conditions. The evaluation results show that ShapeShifter achieves the optimal tradeoff while maintaining robustness.

Zhiqiang Pan, Chen Gao 0001, Fei Cai, Wanyu Chen, Xin Zhang 0123, Honghui Chen, Yong Li 0008

Dynamic link prediction aims to predict the future links on dynamic graphs, which can be applied to wide scenarios such as recommender systems and social networks on the World Wide Web. Existing methods mainly (1) focus on the in-graph learning, which cannot generalize to graphs unobserved during training; or (2) achieve the cross-graph predictions in a many-many mechanism by training on multiple graphs across various domains, which results in a large computational cost. In this paper, we propose a cross-graph dynamic link predictor named CrossDyG, which achieves the cross-graph transferability in a one-many mechanism which trains on one single source graph and test on different target graphs. Specifically, we provide causal and empirical analysis on the structural bias caused by the graph-specific structural characteristics in cross-graph predictions. Then, we conduct deconfounded training to learn the universal network evolution pattern from one single source graph during training. Finally, we apply the causal intervention to leverage the graph-specific structural characteristics of each target graph during inference. Extensive experiments conducted on three benchmark data of dynamic graphs demonstrate that CrossDyG outperforms the state-of-the-art baselines by up to 11.01% and 17.02% in terms of AP and AUC, respectively. In addition, the improvements are especially significant when training on small source graphs.

Peng Zhao 0018, You Zhou 0008, Di Wang 0004, Zhiguang Cao, Yubin Xiao, Xuan Wu 0004, Yuanshu Li, Hongjia Liu, Wei Du 0002, Yuan Jiang 0007 等

With the widespread adoption of Internet Protocol (IP) communication technology and web-based platforms, cloud manufacturing has become a significant hallmark of Industry 4.0. Integrating graph algorithms into these web-enabled environments is crucial as they facilitate the representation and analysis of complex relationships in manufacturing processes, enabling efficient decision-making and adaptability in dynamic environments. As a key scheduling problem in cloud manufacturing, the flexible job-shop scheduling problem (FJSP) finds extensive applications in real-world scenarios. However, traditional FJSP-solving methods struggle to meet the efficiency and adaptability demands of cloud manufacturing due to generalization issues and excessive computational time, while reinforcement learning-based methods fail to learn relationships between FJSP nodes, such as interactions between operations of different jobs, leading to limited interpretability and performance. To address these issues, we propose a dual operation aggregation graph neural network (GNN) for solving FJSP. Specifically, we decouple the disjunctive graph into two distinct graphs, reducing graph density and clarifying relationships between machines and operations, thus enabling more effective aggregation and understanding by neural networks. We develop two distinct graph aggregation methods to minimize the influence of non-critical machine and operation nodes on decision-making while enhancing the model's ability to account for long-term benefits. Additionally, to achieve more accurate multi-objective estimation and mitigate reward sparsity, we design a reward function that simultaneously considers machine efficiency, schedule balance, and makespan minimization. Extensive experimental results on well-known datasets demonstrate that our model outperforms state-of-the-art models and exhibits excellent generalization capabilities, effectively addressing the challenges of cloud manufacturing.

Fangchen Yu, Yicheng Zeng, Jianfeng Mao, Wenye Li 0001

Addressing the critical challenge of data incompleteness in inner product matrix estimation, we introduce a novel eigenvalue correction method designed to precisely reconstruct true inner product matrices from incomplete data. Utilizing random matrix theory, our method adjusts the eigenvalue distribution of the estimated inner product matrix to align with the ground truth. This approach significantly reduces estimation errors for both inner product matrices and the associated Euclidean distance matrices, thereby enhancing the effectiveness of similarity searches on incomplete data. Our method surpasses traditional data imputation and similarity calibration techniques in both maximum inner product search and nearest neighbor search tasks, demonstrating marked advancements in managing incomplete data.

Ruizhe Shi, Ruizhi Cheng, Yuqi Fu, Bo Han 0001, Yue Cheng 0001, Songqing Chen

The InterPlanetary File System (IPFS) is a pioneering effort for Web 3.0, well-known for its decentralized infrastructure. However, some recent studies have shown that IPFS exhibits a high degree of centralization and has integrated centralized components for improved performance. While this change contradicts the core decentralized ethos of IPFS and introduces risks of hurting the data replication level and thus availability, it also opens some opportunities for better data management and cost savings through deduplication. To explore these challenges and opportunities, we start by collecting an extensive dataset of IPFS internal traffic spanning the last three years with 20+ billion messages. By analyzing this long-term trace, we obtain a more complete and accurate view of how the status of centralization evolves over an extended period. In particular, our study reveals that (1) IPFS shows a low replication level, with only 2.71% of data files replicated more than 5 times. While increasing replication enhances lookup performance and data availability, it adversely affects downloading throughput due to the overhead involved in managing peer connections, (2) there is a clear growing trend in centralization within IPFS in the last 3 years, with just 5% of peers now hosting over 80% of the content, significantly decreasing from 21.38% 3 years ago, which is largely driven by the increase of cloud nodes, (3) the default deduplication strategy of IPFS using Fixed-Size Chunking (FSC) is largely inefficient, especially with the default 256KB chunk size, showing near-zero duplication being detected. Although Content-Defined Chunking (CDC) with smaller chunks could save ~1.8 petabytes (PB) storage space, it could impact user performance negatively. We thus design and evaluate a new metadata format that optimizes deduplication without compromising performance.

Asiri Wijesinghe, Hao Zhu 0010, Piotr Koniusz

Traditional Graph Self-Supervised Learning (GSSL) struggles to capture complex structural properties well. This limitation stems from two main factors: (1) the inadequacy of conventional Graph Neural Networks (GNNs) in representing sophisticated topological features, and (2) the focus of self-supervised learning solely on final graph representations. To address these issues, we introduce GenHopNet, a GNN framework that integrates a k-hop message-passing scheme, enhancing its ability to capture local structural information without explicit substructure extraction. We theoretically demonstrate that GenHopNet surpasses the expressiveness of the classical Weisfeiler-Lehman (WL) test for graph isomorphism. Furthermore, we propose a structural- and positional-aware GSSL framework that incorporates topological information throughout the learning process. This approach enables the learning of representations that are both sensitive to graph topology and invariant to specific structural and feature augmentations. Comprehensive experiments on graph classification datasets, including those designed to test structural sensitivity, show that our method consistently outperforms the existing approaches and maintains computational efficiency. Our work significantly advances GSSL's capability in distinguishing graphs with similar local structures but different global topologies.

Qihe Huang, Zhengyang Zhou, Kuo Yang 0002, Yang Wang 0015

The World Wide Web thrives on intelligent services that depend heavily on accurate time series forecasting to navigate dynamic and evolving environments. Due to the partially-observed nature of real world, exclusively focusing on the target of interest, so-called endogenous variables, is insufficient for accurate forecasting, especially in web systems that are susceptible to external influences. Thus, utilizing exogenous variables to harness external information, i.e., forecasting with exogenous variable (FEV), is imperative. Nevertheless, as the external environment is complex and ever-evolving, inadequately capturing external influences can even lead to learning spurious correlations and invalid prediction. Fortunately, recent studies have demonstrated that large language models (LLMs) exhibit exceptional recognition capabilities across open real-world systems, including a deep understanding of exogenous environments. However, it is difficult to directly apply LLMs for FEV due to challenges of task activation, exogenous knowledge extraction, and feature space alignment. In this work, we devise ExoLLM, an LLM-driven method to sufficiently utilize Exogenous variables for time series forecasting. We begin by Meta-task Instruction to activate the knowledge transfer of LLM from natural language processing to FEV. To comprehensively understand the intricate and hierarchical influences of exogenous variables, we propose Multi-grained Prompts, encompassing diverse external influences, including natural attributes, trend correlations, and period relationships between two types of variables. Additionally, a Dual TS-Text Attention is devised to bridge the feature gap between text and numeric data in LLM. Evaluation on real-world datasets demonstrates ExoLLM's superiority in exploiting exogenous information for forecasting with open-world language knowledge.

Roberto Barile, Claudia d'Amato, Nicola Fanizzi

Knowledge Graphs provide a machine-readable representation of knowledge conforming to graph-based data models. Link prediction methods predict missing facts in incomplete knowledge graphs, often using scalable embedding based solutions that, however, lack comprehensibility which is crucial in many domains. Filling this gap, explanation methods identify supporting knowledge. For evaluating them, user studies are the obvious choice as users are the main recipients of explanations. However, finding domain experts is often challenging. In contrast, an automated approach is to measure the influence of explanations on the very same link prediction task, thus disregarding the perspective of users. Additionally, current evaluation methods vary across different explanation approaches. We propose LP-DIXIT, the first protocol to evaluate the utility of explanations of link predictions. LP-DIXIT is user-aware, algorithmic and unique for different explanation methods. It builds on a typical setting of user studies, but adopts Large Language Models (LLMs) to mimic users. Specifically, it measures how explanations improve the user (LLM) ability to perform predictions, which is key to trust. We experimentally proved an overall agreement between LP-DIXIT and user evaluations. Moreover, we adopted LP-DIXIT to conduct a comparative study of state-of-the-art explanation methods. The outcomes suggest that less is more: the most effective explanations are those consisting of a single fact.

Sungwon Park 0001, Sungwon Han 0001, Xing Xie 0001, Jae-Gil Lee 0001, Meeyoung Cha

The spread of fake news harms individuals and presents a critical social challenge that must be addressed. Although numerous algorithmic and insightful features have been developed to detect fake news, many of these features can be manipulated with style-conversion attacks, especially with the emergence of advanced language models, making it more difficult to differentiate from genuine news. This study proposes adversarial style augmentation, AdStyle, designed to train a fake news detector that remains robust against various style-conversion attacks. The primary mechanism involves the strategic use of LLMs to automatically generate a diverse and coherent array of style-conversion attack prompts, enhancing the generation of particularly challenging prompts for the detector. Experiments indicate that our augmentation strategy significantly improves robustness and detection performance when evaluated on fake news benchmark datasets.

Sungjun Jung, Yongsang Park, Haeun Lee, Young H. Oh, Jae W. Lee

Graph-based approximate nearest neighbor search (ANNS) algorithms are widely used to identify the most similar vectors to a given query vector. Graph-based ANNS consists of two stages: constructing a graph and searching on the graph for a given query vector. While reducing the query response time is of great practical importance, less attention has been paid to improving the online search method than the offline graph construction method. This paper provides an extensive experimental analysis on the popular greedy search and other search optimization strategies. We also propose a novel angular distance-guided search method for graph-based ANNS (ADA-NNS) to improve search efficiency. The key innovation of ADA-NNS is introducing a low-cost neighbor selection mechanism based on approximate similarity score derived from angular distance estimation, which effectively filters out less relevant neighbors. We compare state-of-the-art search techniques, including FINGER, on six datasets using different similarity metrics. It provides a comprehensive perspective on their tradeoffs in terms of throughput, latency, and recall. Our evaluation shows that ADA-NNS achieves 34%-107% higher queries per second (QPS) than the greedy search at 95% recall@10 on HNSW, one of the most popular graph structures for ANNS.

Junxiao Ma, Jingjing Wang, Jiamin Luo, Peiying Yu, Guodong Zhou 0001

Prior studies on Video Anomaly Detection (VAD) mainly focus on detecting whether each video frame is abnormal or not in the video, which largely ignore the structured video semantic information (i.e., what, when, and where does the abnormal event happen). With this in mind, this paper propose a new chat-paradigm Multi-scene Video Abnormal Event Extraction and Localization (M-VAE) task, aiming to extract the abnormal event quadruples (i.e., subject, event type, object, scene) and localize such event. Further, this paper believes that this new task faces two key challenges, i.e., global-local spatial modeling and global-local spatial balancing. To this end, this paper proposes a Global-local Spatial-sensitive Large Language Model (LLM) named Sherlock, i.e., acting like Sherlock Holmes to track down the criminal events, for this M-VAE task. Specifically, this model designs a Global-local Spatial-enhanced MoE (GSM) module and a Spatial Imbalance Regulator (SIR) to address the two challenges respectively. Extensive experiments on our M-VAE instruction dataset show the significant advantages of Sherlock over several advanced Video-LLMs. This justifies the importance of global-local spatial information for the M-VAE task and the effectiveness of Sherlock in capturing such information.

Jiaqing Zhang, Mingjia Yin, Hao Wang 0076, Yawen Li 0001, Yuyang Ye 0002, Xingyu Lou, Junping Du 0001, Enhong Chen

In the era of data-centric AI, the focus of recommender systems has shifted from model-centric innovations to data-centric approaches. The success of modern AI models is built on large-scale datasets, but this also results in significant training costs. Dataset distillation has emerged as a key solution, condensing large datasets to accelerate model training while preserving model performance. However, condensing discrete and sequentially correlated user-item interactions, particularly with extensive item sets, presents considerable challenges. This paper introduces TD3, a novel Tucker Decomposition based Dataset Distillation method within a meta-learning framework, designed for sequential recommendation. TD3 distills a fully expressive synthetic sequence summary from original data. To efficiently reduce computational complexity and extract refined latent patterns, Tucker decomposition decouples the summary into four factors: synthetic user latent factor, temporal dynamics latent factor, shared item latent factor, and a relation core that models their interconnections. Additionally, a surrogate objective in bi-level optimization is proposed to align feature spaces extracted from models trained on both original data and synthetic sequence summary beyond the naive performance matching approach. In the inner-loop, an augmentation technique allows the learner to closely fit the synthetic summary, ensuring an accurate update of it in the outer-loop. To accelerate the optimization process and address long dependencies, RaT-BPTT is employed for bi-level optimization. Experiments and analyses on multiple public datasets have confirmed the superiority and cross-architecture generalizability of the proposed designs. Codes are released at https://github.com/USTC-StarTeam/TD3.

Saurabh Sharma, Ambuj K. Singh

The problem of maximizing the adoption of a product through viral marketing in social networks has been studied heavily through postulated network models. We present a novel data-driven formulation of the problem. We use Graph Neural Networks (GNNs) to model the adoption of products by utilizing both topological and attribute information. The resulting Dynamic Viral Marketing (DVM) problem seeks to find the minimum budget and minimal set of dynamic topological and attribute changes in order to attain a specified adoption goal. We show that DVM is NP-Hard and is related to the existing influence maximization problem. Motivated by this connection, we develop the idea of Dynamic Gradient Influencing (DGI) that uses gradient ranking to find optimal perturbations and targets low-budget and high influence non-adopters in discrete steps. We use an efficient strategy for computing node budgets and develop the ''Meta-Influence'' heuristic for assessing a node's downstream influence. We evaluate DGI against multiple baselines and demonstrate gains on average of 24% on budget and 37% on AUC on real-world attributed networks. Our code is publicly available at https://github.com/saurabhsharma1993/dynamic_viral_marketing.

Bhupendra Acharya, Dario Lazzaro, Antonio Emanuele Cinà, Thorsten Holz

With the widespread use of social media, organizations, and individuals use these platforms to raise funds and support causes. Unfortunately, this has led to the rise of scammers in soliciting fraudulent donations. In this study, we conduct a large-scale analysis of donation-based scams on social media platforms. More specifically, we studied profile creation and scam operation fraudulent donation solicitation on X, Instagram, Facebook, YouTube, and Telegram. By collecting data from 151,966 accounts and their 3,053,333 posts related to donations between March 2024 and May 2024, we identified 832 scammers using various techniques to deceive users into making fraudulent donations. Analyzing the fraud communication channels such as phone number, email, and external URL linked, we show that these scamming accounts perform various fraudulent donation schemes, including classic abuse such as fake fundraising website setup, crowdsourcing fundraising, and asking users to communicate via email, phone, and pay via various payment methods. Through collaboration with industry partners PayPal and cryptocurrency abuse database Chainabuse, we further validated the scams and measured the financial losses on these platforms. Our study highlights significant weaknesses in social media platforms' ability to protect users from fraudulent donations. Additionally, we recommended social media platforms, and financial services for taking proactive steps to block these fraudulent activities. Our study provides a foundation for the security community and researchers to automate detecting and mitigating fraudulent donation solicitation on social media platforms.

Yongkang Guo, Yuqing Kong

We consider a robust aggregation problem in the presence of both truthful and adversarial experts. The truthful experts will report their private signals truthfully, while the adversarial experts can report arbitrarily. We assume experts are marginally symmetric in the sense that they share the same common prior and marginal posteriors. The rule maker needs to design an aggregator to predict the true world state from these experts' reports, without knowledge of the underlying information structures or adversarial strategies. We aim to find the optimal aggregator that outputs a forecast minimizing regret under the worst information structure and adversarial strategies. The regret is defined by the difference in expected loss between the aggregator and a benchmark who aggregates optimally given the information structure and reports of truthful experts. We focus on binary states and reports. Under L1 loss, we show that the truncated mean aggregator is optimal. When there are at most k adversaries, this aggregator discards the k lowest and highest reported values and averages the remaining ones. For L2 loss, the optimal aggregators are piecewise linear functions. All the optimalities hold when the ratio of adversaries is bounded above by a value determined by the experts' priors and posteriors. The regret only depends on the ratio of adversaries, not on their total number. For hard aggregators that output a decision, we prove that a random version of the truncated mean is optimal for both L1 and L2. This aggregator randomly follows a remaining value after discarding the k lowest and highest reported values. We extend the hard aggregator to multi-state setting. We evaluate our aggregators numerically in an ensemble learning task. We also obtain negative results for general adversarial aggregation problems under broader information structures and report spaces.