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Distributed Pseudo-Likelihood Method for Community Detection in Large-Scale Networks ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-16 Jiayi Deng, Danyang Huang, Bo Zhang
This paper proposes a distributed pseudo-likelihood method (DPL) to conveniently identify the community structure of large-scale networks. Specifically, we first propose a block-wise splitting method to divide large-scale network data into several subnetworks and distribute them among multiple workers. For simplicity, we assume the classical stochastic block model. Then, the DPL algorithm is iteratively
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A Survey of Trustworthy Representation Learning Across Domains ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Ronghang Zhu, Dongliang Guo, Daiqing Qi, Zhixuan Chu, Xiang Yu, Sheng Li
As AI systems have obtained significant performance to be deployed widely in our daily live and human society, people both enjoy the benefits brought by these technologies and suffer many social issues induced by these systems. To make AI systems good enough and trustworthy, plenty of researches have been done to build guidelines for trustworthy AI systems. Machine learning is one of the most important
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LMACL: Improving Graph Collaborative Filtering with Learnable Model Augmentation Contrastive Learning ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Xinru Liu, Yongjing Hao, Lei Zhao, Guanfeng Liu, Victor S. Sheng, Pengpeng Zhao
Graph collaborative filtering (GCF) has achieved exciting recommendation performance with its ability to aggregate high-order graph structure information. Recently, contrastive learning (CL) has been incorporated into GCF to alleviate data sparsity and noise issues. However, most of the existing methods employ random or manual augmentation to produce contrastive views that may destroy the original
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Social Behavior Analysis in Exclusive Enterprise Social Networks by FastHAND ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Yang Yang, Feifei Wang, Enqiang Zhu, Fei Jiang, Wen Yao
There is an emerging trend in the Chinese automobile industries that automakers are introducing exclusive enterprise social networks (EESNs) to expand sales and provide after-sale services. The traditional online social networks (OSNs) and enterprise social networks (ESNs), such as X (formerly known as Twitter) and Yammer, are ingeniously designed to facilitate unregulated communications among equal
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On Breaking Truss-based and Core-based Communities ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Huiping Chen, Alessio Conte, Roberto Grossi, Grigorios Loukides, Solon P. Pissis, Michelle Sweering
We introduce the general problem of identifying a smallest edge subset of a given graph whose deletion makes the graph community-free. We consider this problem under two community notions that have attracted significant attention: k-truss and k-core. We also introduce a problem variant where the identified subset contains edges incident to a given set of nodes and ensures that these nodes are not contained
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Intricate Spatiotemporal Dependency Learning for Temporal Knowledge Graph Reasoning ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Xuefei Li, Huiwei Zhou, Weihong Yao, Wenchu Li, Baojie Liu, Yingyu Lin
Knowledge Graph (KG) reasoning has been an interesting topic in recent decades. Most current researches focus on predicting the missing facts for incomplete KG. Nevertheless, Temporal KG (TKG) reasoning, which is to forecast future facts, still faces with a dilemma due to the complex interactions between entities over time. This article proposes a novel intricate Spatiotemporal Dependency learning
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ProtoMGAE: Prototype-Aware Masked Graph Auto-Encoder for Graph Representation Learning ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Yimei Zheng, Caiyan Jia
Graph self-supervised representation learning has gained considerable attention and demonstrated remarkable efficacy in extracting meaningful representations from graphs, particularly in the absence of labeled data. Two representative methods in this domain are graph auto-encoding and graph contrastive learning. However, the former methods primarily focus on global structures, potentially overlooking
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Fairness-Aware Graph Neural Networks: A Survey ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 April Chen, Ryan A. Rossi, Namyong Park, Puja Trivedi, Yu Wang, Tong Yu, Sungchul Kim, Franck Dernoncourt, Nesreen K. Ahmed
Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks. Despite this success, GNNs suffer from fairness issues that arise as a result of the underlying graph data and the fundamental aggregation mechanism that lies at the heart of the large class of GNN models. In this article
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Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Acong Zhang, Jincheng Huang, Ping Li, Kai Zhang
Multiple recent studies show a paradox in graph convolutional networks (GCNs)—that is, shallow architectures limit the capability of learning information from high-order neighbors, whereas deep architectures suffer from over-smoothing or over-squashing. To enjoy the simplicity of shallow architectures and overcome their limits of neighborhood extension, in this work we introduce a biaffine technique
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DP-GCN: Node Classification by Connectivity and Local Topology Structure on Real-World Network ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Zhe Chen, Aixin Sun
Node classification is to predict the class label of a node by analyzing its properties and interactions in a network. We note that many existing solutions for graph-based node classification only consider node connectivity but not the node’s local topology structure. However, nodes residing in different parts of a real-world network may share similar local topology structures. For example, local topology
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SsAG: Summarization and Sparsification of Attributed Graphs ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Sarwan Ali, Muhammad Ahmad, Maham Anwer Beg, Imdad Ullah Khan, Safiullah Faizullah, Muhammad Asad Khan
Graph summarization has become integral for managing and analyzing large-scale graphs in diverse real-world applications, including social networks, biological networks, and communication networks. Existing methods for graph summarization often face challenges, being either computationally expensive, limiting their applicability to large graphs, or lacking the incorporation of node attributes. In response
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Multi-Scenario and Multi-Task Aware Feature Interaction for Recommendation System ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Derun Song, Enneng Yang, Guibing Guo, Li Shen, Linying Jiang, Xingwei Wang
Multi-scenario and multi-task recommendation can use various feedback behaviors of users in different scenarios to learn users’ preferences and then make recommendations, which has attracted attention. However, the existing work ignores feature interactions and the fact that a pair of feature interactions will have differing levels of importance under different scenario-task pairs, leading to sub-optimal
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nSimplex Zen: A Novel Dimensionality Reduction for Euclidean and Hilbert Spaces ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Richard Connor, Lucia Vadicamo
Dimensionality reduction techniques map values from a high dimensional space to one with a lower dimension. The result is a space which requires less physical memory and has a faster distance calculation. These techniques are widely used where required properties of the reduced-dimension space give an acceptable accuracy with respect to the original space. Many such transforms have been described.
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Citation Forecasting with Multi-Context Attention-Aided Dependency Modeling ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Taoran Ji, Nathan Self, Kaiqun Fu, Zhiqian Chen, Naren Ramakrishnan, Chang-Tien Lu
Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based
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Node Embedding Preserving Graph Summarization ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Houquan Zhou, Shenghua Liu, Huawei Shen, Xueqi Cheng
Graph summarization is a useful tool for analyzing large-scale graphs. Some works tried to preserve original node embeddings encoding rich structural information of nodes on the summary graph. However, their algorithms are designed heuristically and not theoretically guaranteed. In this article, we theoretically study the problem of preserving node embeddings on summary graph. We prove that three
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Adaptive Content-Aware Influence Maximization via Online Learning to Rank ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 Konstantinos Theocharidis, Panagiotis Karras, Manolis Terrovitis, Spiros Skiadopoulos, Hady W. Lauw
How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence Maximization (IM) problem, which seeks a set of seed users that maximize a post’s influence. However, there is no work on progressively learning how
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Do We Really Need Imputation in AutoML Predictive Modeling? ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-12 George Paterakis, Stefanos Fafalios, Paulos Charonyktakis, Vassilis Christophides, Ioannis Tsamardinos
Numerous real-world data contain missing values, while in contrast, most Machine Learning (ML) algorithms assume complete datasets. For this reason, several imputation algorithms have been proposed to predict and fill in the missing values. Given the advances in predictive modeling algorithms tuned in an Automated Machine Learning context (AutoML) setting, a question that naturally arises is to what
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Congestion-aware Spatio-Temporal Graph Convolutional Network Based A* Search Algorithm for Fastest Route Search ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-11 Hongjie Sui, Huan Yan, Tianyi Zheng, Wenzhen Huang, Yunlin Zhuang, Yong Li
The fastest route search, which is to find a path with the shortest travel time when the user initiates a query, has become one of the most important services in many map applications. To enhance the user experience of travel, it is necessary to achieve accurate and real-time route search. However, traffic conditions are changing dynamically, especially the frequent occurrence of traffic congestion
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FETILDA: An Evaluation Framework for Effective Representations of Long Financial Documents ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-10 Bolun (Namir) Xia, Vipula Rawte, Aparna Gupta, Mohammed Zaki
In the financial sphere, there is a wealth of accumulated unstructured financial data, such as the textual disclosure documents that companies submit on a regular basis to regulatory agencies, such as the Securities and Exchange Commission (SEC). These documents are typically very long and tend to contain valuable soft information about a company’s performance that is not present in quantitative predictors
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Towards Few-Label Vertical Federated Learning ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-09 Lei Zhang, Lele Fu, Chen Liu, Zhao Yang, Jinghua Yang, Zibin Zheng, Chuan Chen
Federated Learning (FL) provided a novel paradigm for privacy-preserving machine learning, enabling multiple clients to collaborate on model training without sharing private data. To handle multi-source heterogeneous data, vertical federated learning (VFL) has been extensively investigated. However, in the context of VFL, the label information tends to be kept in one authoritative client and is very
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Computing Random Forest-distances in the presence of missing data ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-08 Manuele Bicego, Ferdinando Cicalese
In this paper, we study the problem of computing Random Forest-distances in the presence of missing data. We present a general framework which avoids pre-imputation and uses in an agnostic way the information contained in the input points. We centre our investigation on RatioRF, an RF-based distance recently introduced in the context of clustering and shown to outperform most known RF-based distance
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Enhancing Unsupervised Outlier Model Selection: A Study on IREOS Algorithms ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-04-05 Philipp Schlieper, Hermann Luft, Kai Klede, Christoph Strohmeyer, Bjoern Eskofier, Dario Zanca
Outlier detection stands as a critical cornerstone in the field of data mining, with a wide range of applications spanning from fraud detection to network security. However, real-world scenarios often lack labeled data for training, necessitating unsupervised outlier detection methods. This study centers on Unsupervised Outlier Model Selection (UOMS), with a specific focus on the family of Internal
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Towards Robust Rumor Detection with Graph Contrastive and Curriculum Learning ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-30 Wen-Ming Zhuang, Chih-Yao Chen, Cheng-Te Li
Establishing a robust rumor detection model is vital in safeguarding the veracity of information on social media platforms. However, existing approaches to stopping rumor from spreading rely on abundant and clean training data, which is rarely available in real-world scenarios. In this work, we aim to develop a trustworthy rumor detection model that can handle inadequate and noisy labeled data. Our
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Properties of fairness measures in the context of varying class imbalance and protected group ratios ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-28 Dariusz Brzezinski, Julia Stachowiak, Jerzy Stefanowski, Izabela Szczech, Robert Susmaga, Sofya Aksenyuk, Uladzimir Ivashka, Oleksandr Yasinskyi
Society is increasingly relying on predictive models in fields like criminal justice, credit risk management, or hiring. To prevent such automated systems from discriminating against people belonging to certain groups, fairness measures have become a crucial component in socially relevant applications of machine learning. However, existing fairness measures have been designed to assess the bias between
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TOMGPT: Reliable Text-Only Training Approach for Cost-Effective Multi-modal Large Language Model ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-28 Yunkai Chen, Qimeng Wang, Shiwei Wu, Yan Gao, Tong Xu, Yao Hu
Multi-modal large language models (MLLMs), such as GPT-4, exhibit great comprehension capabilities on human instruction, as well as zero-shot ability on new downstream multi-modal tasks. To integrate the different modalities within a unified embedding space, previous MLLMs attempted to conduct visual instruction tuning with massive and high-quality image-text pair data, which requires substantial costs
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Attacking Social Media via Behavior Poisoning ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-27 Chenwang Wu, Defu Lian, Yong Ge, Min Zhou, Enhong Chen
Since social media such as Facebook and Twitter have permeated various aspects of daily life, people have strong incentives to influence information dissemination on these platforms and differentiate their content from the fierce competition. Existing dissemination strategies typically employ marketing techniques, such as seeking publicity through renowned actors or targeted advertising placements
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On the Value of Head Labels in Multi-Label Text Classification ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Haobo Wang, Cheng Peng, Hede Dong, Lei Feng, Weiwei Liu, Tianlei Hu, Ke Chen, Gang Chen
A formidable challenge in the multi-label text classification (MLTC) context is that the labels often exhibit a long-tailed distribution, which typically prevents deep MLTC models from obtaining satisfactory performance. To alleviate this problem, most existing solutions attempt to improve tail performance by means of sampling or introducing extra knowledge. Data-rich labels, though more trustworthy
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Incorporating Multi-Level Sampling with Adaptive Aggregation for Inductive Knowledge Graph Completion ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Kai Sun, Huajie Jiang, Yongli Hu, Baocai Yin
In recent years, Graph Neural Networks (GNNs) have achieved unprecedented success in handling graph-structured data, thereby driving the development of numerous GNN-oriented techniques for inductive knowledge graph completion (KGC). A key limitation of existing methods, however, is their dependence on pre-defined aggregation functions, which lack the adaptability to diverse data, resulting in suboptimal
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SA2E-AD: A Stacked Attention Autoencoder for Anomaly Detection in Multivariate Time Series ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Mengyao Li, Zhiyong Li, Zhibang Yang, Xu Zhou, Yifan Li, Ziyan Wu, Lingzhao Kong, Ke Nai
Anomaly detection for multivariate time series is an essential task in the modern industrial field. Although several methods have been developed for anomaly detection, they usually fail to effectively exploit the metrical-temporal correlation and the other dependencies among multiple variables. To address this problem, we propose a stacked attention autoencoder for anomaly detection in multivariate
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Automatically Inspecting Thousands of Static Bug Warnings with Large Language Model: How Far Are We? ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Cheng Wen, Yuandao Cai, Bin Zhang, Jie Su, Zhiwu Xu, Dugang Liu, Shengchao Qin, Zhong Ming, Cong Tian
Static analysis tools for capturing bugs and vulnerabilities in software programs are widely employed in practice, as they have the unique advantages of high coverage and independence from the execution environment. However, existing tools for analyzing large codebases often produce a great deal of false warnings over genuine bug reports. As a result, developers are required to manually inspect and
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Dual Homogeneity Hypergraph Motifs with Cross-view Contrastive Learning for Multiple Social Recommendations ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Jiadi Han, Yufei Tang, Qian Tao, Yuhan Xia, LiMing Zhang
Social relations are often used as auxiliary information to address data sparsity and cold-start issues in social recommendations. In the real world, social relations among users are complex and diverse. Widely used graph neural networks (GNNs) can only model pairwise node relationships and are not conducive to exploring higher-order connectivity, while hypergraph provides a natural way to model high-order
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X-FSPMiner: A Novel Algorithm for Frequent Similar Pattern Mining ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Ansel Y. Rodríguez-González, Ramón Aranda, Miguel Á. Álvarez-Carmona, Angel Díaz-Pacheco, Rosa María Valdovinos Rosas
Frequent similar pattern mining (FSP mining) allows for finding frequent patterns hidden from the classical approach. However, the use of similarity functions implies more computational effort, necessitating the development of more efficient algorithms for FSP mining. This work aims to improve the efficiency of mining all FSPs when using Boolean and non-increasing monotonic similarity functions. A
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Multi-Instance Learning with One Side Label Noise ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Tianxiang Luan, Shilin Gu, Xijia Tang, Wenzhang Zhuge, Chenping Hou
Multi-instance Learning (MIL) is a popular learning paradigm arising from many real applications. It assigns a label to a set of instances, which is called a bag, and the bag’s label is determined by the instances within it. A bag is positive if and only if it has at least one positive instance. Since labeling bags is more complicated than labeling each instance, we will often face the mislabeling
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Math Word Problem Generation via Disentangled Memory Retrieval ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Wei Qin, Xiaowei Wang, Zhenzhen Hu, Lei Wang, Yunshi Lan, Richang Hong
The task of math word problem (MWP) generation, which generates an MWP given an equation and relevant topic words, has increasingly attracted researchers’ attention. In this work, we introduce a simple memory retrieval module to search related training MWPs, which are used to augment the generation. To retrieve more relevant training data, we also propose a disentangled memory retrieval module based
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Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Wentao Hu, Hui Fang
With increasing frequency of high-profile privacy breaches in various online platforms, users are becoming more concerned about their privacy. And recommender system is the core component of online platforms for providing personalized service, consequently, its privacy preservation has attracted great attention. As the gold standard of privacy protection, differential privacy has been widely adopted
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Local Community Detection in Multiple Private Networks ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Li Ni, Rui Ye, Wenjian Luo, Yiwen Zhang
Individuals are often involved in multiple online social networks. Considering that owners of these networks are unwilling to share their networks, some global algorithms combine information from multiple networks to detect all communities in multiple networks without sharing their edges. When data owners are only interested in the community containing a given node, it is unnecessary and computationally
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Enhancing Out-of-distribution Generalization on Graphs via Causal Attention Learning ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Yongduo Sui, Wenyu Mao, Shuyao Wang, Xiang Wang, Jiancan Wu, Xiangnan He, Tat-Seng Chua
In graph classification, attention- and pooling-based graph neural networks (GNNs) predominate to extract salient features from the input graph and support the prediction. They mostly follow the paradigm of “learning to attend,” which maximizes the mutual information between the attended graph and the ground-truth label. However, this paradigm causes GNN classifiers to indiscriminately absorb all statistical
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A Taxonomy for Learning with Perturbation and Algorithms ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Rujing Yao, Ou Wu
Weighting strategy prevails in machine learning. For example, a common approach in robust machine learning is to exert low weights on samples which are likely to be noisy or quite hard. This study summarizes another less-explored strategy, namely, perturbation. Various incarnations of perturbation have been utilized but it has not been explicitly revealed. Learning with perturbation is called perturbation
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Generation-based Multi-view Contrast for Self-supervised Graph Representation Learning ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Yuehui Han
Graph contrastive learning has made remarkable achievements in the self-supervised representation learning of graph-structured data. By employing perturbation function (i.e., perturbation on the nodes or edges of graph), most graph contrastive learning methods construct contrastive samples on the original graph. However, the perturbation-based data augmentation methods randomly change the inherent
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Mining Top-k High On-shelf Utility Itemsets Using Novel Threshold Raising Strategies ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Kuldeep Singh, Bhaskar Biswas
High utility itemsets (HUIs) mining is an emerging area of data mining which discovers sets of items generating a high profit from transactional datasets. In recent years, several algorithms have been proposed for this task. However, most of them do not consider the on-shelf time period of items and negative utility of items. High on-shelf utility itemset (HOUIs) mining is more difficult than traditional
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Conditional Generative Adversarial Network for Early Classification of Longitudinal Datasets Using an Imputation Approach ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Sharon Torao Pingi, Richi Nayak, Md Abul Bashar
Early classification of longitudinal data remains an active area of research today. The complexity of these datasets and the high rates of missing data caused by irregular sampling present data-level challenges for the Early Longitudinal Data Classification (ELDC) problem. Coupled with the algorithmic challenge of optimising the opposing objectives of early classification (i.e., earliness and accuracy)
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Scalable and Inductive Semi-supervised Classifier with Sample Weighting Based on Graph Topology ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-26 Fadi Dornaika, Zoulfikar Ibrahim, Alirezah Bosaghzadeh
Recently, graph-based semi-supervised learning (GSSL) has garnered significant interest in the realms of machine learning and pattern recognition. Although some of the proposed methods have made some progress, there are still some shortcomings that need to be overcome. There are three main limitations. First, the graphs used in these approaches are usually predefined regardless of the task at hand
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Hierarchical Convolutional Neural Network with Knowledge Complementation for Long-Tailed Classification ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-22 Hong Zhao, Zhengyu Li, Wenwei He, Yan Zhao
Existing methods based on transfer learning leverage auxiliary information to help tail generalization and improve the performance of the tail classes. However, they cannot fully exploit the relationships between auxiliary information and tail classes and bring irrelevant knowledge to the tail classes. To solve this problem, we propose a hierarchical CNN with knowledge complementation, which regards
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Multi-Source and Multi-modal Deep Network Embedding for Cross-Network Node Classification ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-20 Hongwei Yang, Hui He, Weizhe Zhang, Yan Wang, Lin Jing
In recent years, to address the issue of networked data sparsity in node classification tasks, cross-network node classification (CNNC) leverages the richer information from a source network to enhance the performance of node classification in the target network, which typically has sparser information. However, in real-world applications, labeled nodes may be collected from multiple sources with multiple
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NOODLE: Joint Cross-View Discrepancy Discovery and High-Order Correlation Detection for Multi-View Subspace Clustering ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-20 Zhibin Gu, Songhe Feng, Zhendong Li, Jiazheng Yuan, Jun Liu
Benefiting from the effective exploration of the valuable topological pair-wise relationship of data points across multiple views, multi-view subspace clustering (MVSC) has received increasing attention in recent years. However, we observe that existing MVSC approaches still suffer from two limitations that need to be further improved to enhance the clustering effectiveness. Firstly, previous MVSC
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Representative and Back-In-Time Sampling from Real-World Hypergraphs ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-19 Minyoung Choe, Jaemin Yoo, Geon Lee, Woonsung Baek, U Kang, Kijung Shin
Graphs are widely used for representing pairwise interactions in complex systems. Since such real-world graphs are large and often evergrowing, sampling subgraphs is useful for various purposes, including simulation, visualization, stream processing, representation learning, and crawling. However, many complex systems consist of group interactions (e.g., collaborations of researchers and discussions
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A Dual Perspective Framework of Knowledge-correlation for Cross-domain Recommendation ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-18 Yuhan Wang, Qing Xie, Mengzi Tang, Lin Li, Jingling Yuan, Yongjian Liu
Recommender System provides users with online services in a personalized way. The performance of traditional recommender systems may deteriorate because of problems such as cold-start and data sparsity. Cross-domain Recommendation System utilizes the richer information from auxiliary domains to guide the task in the target domain. However, direct knowledge transfer may lead to a negative impact due
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Semi-supervised Multi-view Clustering based on Nonnegative Matrix Factorization with Fusion Regularization ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-18 Guosheng Cui, Ruxin Wang, Dan Wu, Ye Li
Multi-view clustering has attracted significant attention and application. Nonnegative matrix factorization is one popular feature learning technology in pattern recognition. In recent years, many semi-supervised nonnegative matrix factorization algorithms are proposed by considering label information, which has achieved outstanding performance for multi-view clustering. However, most of these existing
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FulBM: Fast fully batch maintenance for landmark-based 3-hop cover labeling ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-15 Wentai Zhang, HaiHong E, HaoRan Luo, Mingzhi Sun
Landmark-based 3-hop cover labeling is a category of approaches for shortest distance/path queries on large-scale complex networks. It pre-computes an index offline to accelerate the online distance/path query. Most real-world graphs undergo rapid changes in topology, which makes index maintenance on dynamic graphs necessary. So far, the majority of index maintenance methods can handle only one edge
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DeepMeshCity: A Deep Learning Model for Urban Grid Prediction ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-15 Chi Zhang, Linhao Cai, Meng Chen, Xiucheng Li, Gao Cong
Urban grid prediction can be applied to many classic spatial-temporal prediction tasks such as air quality prediction, crowd density prediction, and traffic flow prediction, which is of great importance to smart city building. In light of its practical values, many methods have been developed for it and have achieved promising results. Despite their successes, two main challenges remain open: a) how
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EffCause: Discover Dynamic Causal Relationships Efficiently from Time-Series ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-28 Yicheng Pan, Yifan Zhang, Xinrui Jiang, Meng Ma, Ping Wang
Since the proposal of Granger causality, many researchers have followed the idea and developed extensions to the original algorithm. The classic Granger causality test aims to detect the existence of the static causal relationship. Notably, a fundamental assumption underlying most previous studies is the stationarity of causality, which requires the causality between variables to keep stable. However
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Asymmetric Learning for Graph Neural Network based Link Prediction ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-27 Kai-Lang Yao, Wu-Jun Li
Link prediction is a fundamental problem in many graph-based applications, such as protein-protein interaction prediction. Recently, graph neural network (GNN) has been widely used for link prediction. However, existing GNN-based link prediction (GNN-LP) methods suffer from scalability problem during training for large-scale graphs, which has received little attention from researchers. In this paper
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Multi-Task Learning with Sequential Dependence Toward Industrial Applications: A Systematic Formulation ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-28 Xiaobo Guo, Mingming Ha, Xuewen Tao, Shaoshuai Li, Youru Li, Zhenfeng Zhu, Zhiyong Shen, Li Ma
Multi-task learning (MTL) is widely used in the online recommendation and financial services for multi-step conversion estimation, but current works often overlook the sequential dependence among tasks. In particular, sequential dependence multi-task learning (SDMTL) faces challenges in dealing with complex task correlations and extracting valuable information in real-world scenarios, leading to negative
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Package Arrival Time Prediction via Knowledge Distillation Graph Neural Network ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-28 Lei Zhang, Yong Liu, Zhiwei Zeng, Yiming Cao, Xingyu Wu, Yonghui Xu, Zhiqi Shen, Lizhen Cui
Accurately estimating packages’ arrival time in e-commerce can enhance users’ shopping experience and improve the placement rate of products. This problem is often formalized as an Origin-Destination (OD)-based ETA (i.e., estimated time of arrival) prediction task, where the delivery time is estimated mainly based on sender and receiver addresses and other context information. One inherent challenge
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Correlation-aware Graph Data Augmentation with Implicit and Explicit Neighbors ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-27 Chuan-Wei Kuo, Bo-Yu Chen, Wen-Chih Peng, Chih-Chieh Hung, Hsin-Ning Su
In recent years, there has been a significant surge in commercial demand for citation graph-based tasks, such as patent analysis, social network analysis, and recommendation systems. Graph Neural Networks (GNNs) are widely used for these tasks due to their remarkable performance in capturing topological graph information. However, GNNs’ output results are highly dependent on the composition of local
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Attacking Click-through Rate Predictors via Generating Realistic Fake Samples ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-28 Mingxing Duan, Kenli Li, Weinan Zhang, Jiarui Qin, Bin Xiao
How to construct imperceptible (realistic) fake samples is critical in adversarial attacks. Due to the sample feature diversity of a recommender system (containing both discrete and continuous features), traditional gradient-based adversarial attack methods may fail to construct realistic fake samples. Meanwhile, most recommendation models adopt click-through rate (CTR) predictors, which usually utilize
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Prerequisite-Enhanced Category-Aware Graph Neural Networks for Course Recommendation ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-28 Jianshan Sun, Suyuan Mei, Kun Yuan, Yuanchun Jiang, Jie Cao
The rapid development of Massive Open Online Courses (MOOCs) platforms has created an urgent need for an efficient personalized course recommender system that can assist learners of all backgrounds and levels of knowledge in selecting appropriate courses. Currently, most existing methods utilize a sequential recommendation paradigm that captures the user’s learning interests from their learning history
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TaSPM: Targeted Sequential Pattern Mining ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-28 Gengsen Huang, Wensheng Gan, Philip S. Yu
Sequential pattern mining (SPM) is an important technique in the field of pattern mining, which has many applications in reality. Although many efficient SPM algorithms have been proposed, there are few studies that can focus on targeted tasks. Targeted querying of the concerned sequential patterns can not only reduce the number of patterns generated, but also increase the efficiency of users in performing
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Networked Time-series Prediction with Incomplete Data via Generative Adversarial Network ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-28 Yichen Zhu, Bo Jiang, Haiming Jin, Mengtian Zhang, Feng Gao, Jianqiang Huang, Tao Lin, Xinbing Wang
A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation to environment monitoring to smart grid management. An important task in such applications is to predict the future values of a NETS based on its historical values and the underlying graph. Most existing methods require complete data for
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CoBjeason: Reasoning Covered Object in Image by Multi-Agent Collaboration Based on Informed Knowledge Graph ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-28 Huan Rong, Minfeng Qian, Tinghuai Ma, Di Jin, Victor S. Sheng
Object detection is a widely studied problem in existing works. However, in this paper, we turn to a more challenging problem of “Covered Object Reasoning”, aimed at reasoning the category label of target object in the given image particularly when it has been totally covered (or invisible). To resolve this problem, we propose CoBjeason to seize the opportunity when visual reasoning meets the knowledge