Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management最新文献

{"title":"MUSE: A Multi-slice Joint Analysis Method for Spatial Transcriptomics Experiments.","authors":"Ziheng Duan, Xi Li, Zhiqing Xiao, Rex Ying, Jing Zhang","doi":"10.1145/3746252.3761240","DOIUrl":"10.1145/3746252.3761240","url":null,"abstract":"<p><p>Recent advances in spatial transcriptomics (ST) and cost reductions have enabled large-scale multi-slice ST data generation, enhancing the statistical power to detect subtle biological signals. However, cross-slice inconsistencies and data quality variability present significant analytical challenges. To overcome these limitations, we developed MUSE, a computational framework designed for multislice joint embedding, spatial domain identification, and gene expression imputation. Specifically, MUSE integrates a two-module architecture to ensure robust cross-slice alignment and data harmonization. The alignment module models each slice as a graph and employs optimal transport to align cells across slices while preserving spatial continuity. The optimization module further refines integration by incorporating an alignment loss, allowing lower-quality data to leverage structural information from higher-quality slices. Additionally, MUSE generates virtual neighbors from aligned cells, enriching contextual information and mitigating data sparsity. These design principles enable seamless integration with existing single-slice methods, extending their applicability to multi-slice ST analysis. To comprehensively evaluate its performance, we applied MUSE to 12 real and 48 simulated datasets spanning a range of data qualities. Across all metrics, MUSE consistently outperformed existing methods in cross-slice consistency, spatial domain identification, and gene expression imputation. To promote accessibility and adoption, we provide MUSE as an open-source software package. As multi-slice ST datasets become increasingly prevalent, MUSE provides a robust and extensible framework designed to effectively integrate growing numbers of slices, thereby advancing the analysis of tissue architectures and spatial gene expression in complex biological systems.</p>","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"2025 ","pages":"625-634"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12790625/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145960854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing.","authors":"Dongliang Guo, Mengxuan Hu, Zihan Guan, Junfeng Guo, Thomas Hartvigsen, Sheng Li","doi":"10.1145/3746252.3761408","DOIUrl":"10.1145/3746252.3761408","url":null,"abstract":"<p><p>Large pre-trained models have achieved notable success across a range of downstream tasks. However, recent research shows that a type of adversarial attack (<i>i.e.,</i> backdoor attack) can manipulate the behavior of machine learning models through contaminating their training dataset, posing significant threat in the real-world application of large pre-trained model, especially for those customized models. Therefore, addressing the unique challenges for exploring vulnerability of pre-trained models is of paramount importance. Through empirical studies on the capability for performing backdoor attack in large pre-trained models (<i>e.g.,</i> ViT), we find the following unique challenges of attacking large pre-trained models: 1) the inability to manipulate or even access large training datasets, and 2) the substantial computational resources required for training or fine-tuning these models. To address these challenges, we establish new standards for an effective and feasible backdoor attack in the context of large pre-trained models. In line with these standards, we introduce our EDT model, an <b>E</b>fficient, <b>D</b>ata-free, <b>T</b>raining-free backdoor attack method. Inspired by model editing techniques, EDT injects an editing-based lightweight codebook into the backdoor of large pre-trained models, which replaces the embedding of the poisoned image with the target image without poisoning the training dataset or training the victim model. Our experiments, conducted across various pre-trained models such as ViT, CLIP, BLIP, and stable diffusion, and on downstream tasks including image classification, image captioning, and image generation, demonstrate the effectiveness of our method. Our code is available at https://github.com/donglgcn/Editing/.</p>","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"2025 ","pages":"750-760"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12703712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145770243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transferable Deep Clustering Model.","authors":"Zheng Zhang, Liang Zhao","doi":"10.1145/3746252.3761188","DOIUrl":"10.1145/3746252.3761188","url":null,"abstract":"<p><p>Deep learning has shown remarkable success in the field of clustering recently. However, how to transfer a trained clustering model on a source domain to a target domain by leveraging the acquired knowledge to guide the clustering process remains challenging. Existing deep clustering methods often lack generalizability to new domains because they typically learn a group of fixed cluster centroids, which may not be optimal for the new domain distributions. In this paper, we propose a novel transferable deep clustering model that can automatically adapt the cluster centroids according to the distribution of data samples. Rather than learning a fixed set of centroids, our approach introduces a novel attention-based module that can adapt the centroids by measuring their relationship with samples. In addition, we theoretically show that our model is strictly more powerful than some classical clustering algorithms such as k-means or Gaussian Mixture Model (GMM). Experimental results on both synthetic and real-world datasets demonstrate the effectiveness and efficiency of our proposed transfer learning framework, which significantly improves the performance on target domain and reduces the computational cost.</p>","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"2025 ","pages":"4273-4282"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13499444/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multimodal Large Language Models.","authors":"Mengdan Zhu, Raasikh Kanjiani, Jiahui Lu, Andrew Choi, Qirui Ye, Liang Zhao","doi":"10.1145/3746252.3761318","DOIUrl":"10.1145/3746252.3761318","url":null,"abstract":"<p><p>Deep generative models like VAEs and diffusion models have advanced various generation tasks by leveraging latent variables to learn data distributions and generate high-quality samples. Despite the field of explainable AI making strides in interpreting machine learning models, understanding latent variables in generative models remains challenging. This paper introduces <i>LatentExplainer</i> ¹, a framework for automatically generating semantically meaningful explanations of latent variables in deep generative models. <i>LatentExplainer</i> tackles three main challenges: inferring the meaning of latent variables, aligning explanations with inductive biases, and handling varying degrees of explainability. Our approach perturbs latent variables, interprets changes in generated data, and uses multimodal large language models (MLLMs) to produce human-understandable explanations. We evaluate our proposed method on several real-world and synthetic datasets, and the results demonstrate superior performance in generating high-quality explanations for latent variables. The results highlight the effectiveness of incorporating inductive biases and uncertainty quantification, significantly enhancing model interpretability.</p>","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"2025 ","pages":"4529-4538"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13499445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaults.","authors":"Dong Hyun Jeon, Lijing Zhu, Haifang Li, Pengze Li, Jingna Feng, Tiehang Duan, Houbing Herbert Song, Cui Tao, Shuteng Niu","doi":"10.1145/3746252.3761282","DOIUrl":"10.1145/3746252.3761282","url":null,"abstract":"<p><p>Temporal Graph Neural Networks (TGNNs) have become indispensable for analyzing dynamic graphs in critical applications such as social networks, communication systems, and financial networks. However, the robustness of TGNNs against adversarial attacks, particularly sophisticated attacks that exploit the temporal dimension, remains a significant challenge. Existing attack methods for Spatio-Temporal Dynamic Graphs (STDGs) often rely on simplistic, easily detectable perturbations (e.g., random edge additions/deletions) and fail to strategically target the most influential nodes and edges for maximum impact. We introduce the High Impact Attack (HIA), a novel restricted black-box attack framework specifically designed to overcome these limitations and expose critical vulnerabilities in TGNNs. HIA leverages a data-driven surrogate model to identify structurally important nodes (central to network connectivity) and dynamically important nodes (critical for the graph's temporal evolution). It then employs a hybrid perturbation strategy, combining strategic edge injection (to create misleading connections) and targeted edge deletion (to disrupt essential pathways), maximizing TGNN performance degradation. Importantly, HIA minimizes the number of perturbations to enhance stealth, making it more challenging to detect. Comprehensive experiments on five real-world datasets and four representative TGNN architectures (TGN, JODIE, DySAT, and TGAT) demonstrate that HIA significantly reduces TGNN accuracy on the link prediction task, achieving up to a 35.55% decrease in Mean Reciprocal Rank (MRR) - a substantial improvement over state-of-the-art baselines. These results highlight fundamental vulnerabilities in current STDG models and underscore the urgent need for robust defenses that account for both structural and temporal dynamics. Code and Data are available at https://github.com/ryandhjeon/hia.</p>","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"2025 ","pages":"1035-1045"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147625056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"iMIRACLE: an Iterative Multi-View Graph Neural Network to Model Intercellular Gene Regulation from Spatial Transcriptomic Data.","authors":"Ziheng Duan, Siwei Xu, Cheyu Lee, Dylan Riffle, Jing Zhang","doi":"10.1145/3627673.3679574","DOIUrl":"10.1145/3627673.3679574","url":null,"abstract":"<p><p>Spatial transcriptomics has transformed genomic research by measuring spatially resolved gene expressions, allowing us to investigate how cells adapt to their microenvironment via modulating their expressed genes. This essential process usually starts from cell-cell communication (CCC) via ligand-receptor (LR) interaction, leading to regulatory changes within the receiver cell. However, few methods were developed to connect them to provide biological insights into intercellular regulation. To fill this gap, we propose iMiracle, an iterative multi-view graph neural network that models each cell's intercellular regulation with three key features. Firstly, iMiracle integrates inter- and intra-cellular networks to jointly estimate <i>cell-type</i>- and <i>micro-environment</i>-driven gene expressions. Optionally, it allows prior knowledge of intra-cellular networks as pre-structured masks to maintain biological relevance. Secondly, iMiracle employs iterative learning to overcome the sparsity of spatial transcriptomic data and gradually fill in the missing edges in the CCC network. Thirdly, iMiracle infers a cell-specific ligand-gene regulatory score based on the contributions of different LR pairs to interpret inter-cellular regulation. We applied iMiracle to nine simulated and eight real datasets from three sequencing platforms and demonstrated that iMiracle consistently outperformed ten methods in gene expression imputation and four methods in regulatory score inference. Lastly, we developed iMiracle as an open-source software and anticipate that it can be a powerful tool in decoding the complexities of inter-cellular transcriptional regulation.</p>","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"2024 ","pages":"538-548"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Privacy Bound for Shuffle Model with Personalized Privacy.","authors":"Yixuan Liu, Yuhan Liu, Li Xiong, Yujie Gu, Hong Chen","doi":"10.1145/3627673.3679911","DOIUrl":"10.1145/3627673.3679911","url":null,"abstract":"<p><p>The shuffle model of Differential Privacy (DP) is an enhanced privacy protocol which significantly amplifies the central DP guarantee by anonymizing and shuffling the local randomized data. Yet, deriving a tight privacy bound is challenging due to its complicated randomization protocol. While most existing works focused on uniform local privacy settings, this work focuses on a more practical personalized privacy setting. To bound the privacy after shuffling, we need to capture the probability of each user generating clones of the neighboring data points and quantify the indistinguishability between two distributions of the number of clones on neighboring datasets. Existing works either inaccurately capture the probability or underestimate the indistinguishability. We develop a more precise analysis, which yields a general and tighter bound for arbitrary DP mechanisms. Firstly, we derive the clone-generating probability by hypothesis testing, which leads to a more accurate characterization of the probability. Secondly, we analyze the indistinguishability in the context of <math><mi>f</mi></math> -DP, where the convexity of the distributions is leveraged to achieve a tighter privacy bound. Theoretical and numerical results demonstrate that our bound remarkably outperforms the existing results in the literature. The code is publicly available at https://github.com/Emory-AIMS/HPS.git.</p>","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"2024 ","pages":"3907-3911"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12094779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"scACT: Accurate Cross-modality Translation via Cycle-consistent Training from Unpaired Single-cell Data.","authors":"Siwei Xu, Junhao Liu, Jing Zhang","doi":"10.1145/3627673.3679576","DOIUrl":"10.1145/3627673.3679576","url":null,"abstract":"<p><p>Single-cell sequencing technologies have revolutionized genomics by enabling the simultaneous profiling of various molecular modalities within individual cells. Their integration, especially cross-modality translation, offers deep insights into cellular regulatory mechanisms. Many methods have been developed for cross-modality translation, but their reliance on scarce high-quality co-assay data limits their applicability. Addressing this, we introduce scACT, a deep generative model designed to extract cross-modality biological insights from unpaired single-cell data. scACT tackles three major challenges: aligning unpaired multi-modal data via adversarial training, facilitating cross-modality translation without prior knowledge via cycle-consistent training, and enabling interpretable regulatory interconnections explorations via in-silico perturbations. To test its performance, we applied scACT on diverse single-cell datasets and found it outperformed existing methods in all three tasks. Finally, we have developed scACT as an individual open-source software package to advance single-cell omics data processing and analysis within the research community.</p>","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"2024 ","pages":"2722-2731"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11611688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HypMix: Hyperbolic Representation Learning for Graphs with Mixed Hierarchical and Non-hierarchical Structures.","authors":"Eric W Lee, Bo Xiong, Carl Yang, Joyce C Ho","doi":"10.1145/3627673.3679940","DOIUrl":"10.1145/3627673.3679940","url":null,"abstract":"<p><p>Heterogeneous networks contain multiple types of nodes and links, with some link types encapsulating hierarchical structure over entities. Hierarchical relationships can codify information such as subcategories or one entity being subsumed by another and are often used for organizing conceptual knowledge into a tree-structured graph. Hyperbolic embedding models learn node representations in a hyperbolic space suitable for preserving the hierarchical structure. Unfortunately, current hyperbolic embedding models only implicitly capture the hierarchical structure, failing to distinguish between node types, and they only assume a single tree. In practice, many networks contain a mixture of hierarchical and non-hierarchical structures, and the hierarchical relations may be represented as multiple trees with complex structures, such as sharing certain entities. In this work, we propose a new hyperbolic representation learning model that can handle complex hierarchical structures and also learn the representation of both hierarchical and non-hierarchic structures. We evaluate our model on several datasets, including identifying relevant articles for a systematic review, which is an essential tool for evidence-driven medicine and node classification.</p>","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"2024 ","pages":"3852-3856"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11867734/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation.","authors":"Baoyu Jing, Dawei Zhou, Kan Ren, Carl Yang","doi":"10.1145/3627673.3679642","DOIUrl":"10.1145/3627673.3679642","url":null,"abstract":"<p><p>Spatiotemporal time series are usually collected via monitoring sensors placed at different locations, which usually contain missing values due to various failures, such as mechanical damages and Internet outages. Imputing the missing values is crucial for analyzing time series. When recovering a specific data point, most existing methods consider all the information relevant to that point regardless of the cause-and-effect relationship. During data collection, it is inevitable that some unknown confounders are included, e.g., background noise in time series and non-causal shortcut edges in the constructed sensor network. These confounders could open backdoor paths and establish non-causal correlations between the input and output. Over-exploiting these non-causal correlations could cause overfitting. In this paper, we first revisit spatiotemporal time series imputation from a causal perspective and show how to block the confounders via the frontdoor adjustment. Based on the results of frontdoor adjustment, we introduce a novel Causality-Aware Spatiotemporal Graph Neural Network (Casper), which contains a novel Prompt Based Decoder (PBD) and a Spatiotemporal Causal Attention (SCA). PBD could reduce the impact of confounders and SCA could discover the sparse causal relationships among embeddings. Theoretical analysis reveals that SCA discovers causal relationships based on the values of gradients. We evaluate Casper on three real-world datasets, and the experimental results show that Casper could outperform the baselines and could effectively discover the causal relationships.</p>","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"2024 ","pages":"1027-1037"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}