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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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Node Embedding Preserving Graph Summarization ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-08 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 paper, we theoretically study the problem of preserving node embeddings on summary graph. We prove that three matrix-factorization
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Adaptive Content-Aware Influence Maximization via Online Learning to Rank ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-08 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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Multi-Scenario and Multi-Task Aware Feature Interaction for Recommendation System ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-06 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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SsAG: Summarization and Sparsification of Attributed Graphs ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-06 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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Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-03-01 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, while 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 Biaffine technique
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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
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Graph Time-series Modeling in Deep Learning: A Survey ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-28 Hongjie Chen, Hoda Eldardiry
Time-series and graphs have been extensively studied for their ubiquitous existence in numerous domains. Both topics have been separately explored in the field of deep learning. For time-series modeling, recurrent neural networks or convolutional neural networks model the relations between values across timesteps, while for graph modeling, graph neural networks model the inter-relations between nodes
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MoMENt: Marked Point Processes with Memory-Enhanced Neural Networks for User Activity Modeling ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-29 Sherry Sahebi, Mengfan Yao, Siqian Zhao, Reza Feyzi Behnagh
Marked temporal point process models (MTPPs) aim to model event sequences and event markers (associated features) in continuous time. These models have been applied to various application domains where capturing event dynamics in continuous time is beneficial, such as education systems, social networks, and recommender systems. However, current MTPPs suffer from two major limitations, i.e., inefficient
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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 : 2024-02-28 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 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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Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-28 Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Shaochen Zhong, Bing Yin, Xia Hu
This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks. We provide discussions and insights into the usage of LLMs from the perspectives of models, data, and downstream tasks. Firstly, we offer an introduction and brief summary of current language models. Then, we discuss
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A Fully Test-Time Training Framework for Semi-Supervised Node Classification on Out-of-Distribution Graphs ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-26 Jiaxin Zhang, Yiqi Wang, Xihong Yang, En Zhu
Graph neural networks (GNNs) have shown great potential in representation learning for various graph tasks. However, the distribution shift between the training and test sets poses a challenge to the efficiency of GNNs. To address this challenge, HomoTTT propose a fully test-time training (FTTT) framework for GNNs to enhance the model’s generalization capabilities for node classification tasks. Specifically
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FiFrauD: Unsupervised Financial Fraud Detection in Dynamic Graph Streams ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-27 Samira Khodabandehlou, Alireza Hashemi Golpayegani
Given a stream of financial transactions between traders in an e-market, how can we accurately detect fraudulent traders and suspicious behaviors in real time? Despite the efforts made in detecting these fraudsters, this field still faces serious challenges, including the ineffectiveness of existing methods for the complex and streaming environment of e-markets. As a result, it is still difficult to
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Domain Generalization in Time Series Forecasting ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-27 Songgaojun Deng, Olivier Sprangers, Ming Li, Sebastian Schelter, Maarten de Rijke
Domain generalization aims to design models that can effectively generalize to unseen target domains by learning from observed source domains. Domain generalization poses a significant challenge for time series data, due to varying data distributions and temporal dependencies. Existing approaches to domain generalization are not designed for time series data, which often results in suboptimal or unstable
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X-distribution: Retraceable Power-law Exponent of Complex Networks ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-27 Pradumn Kumar Pandey, Aikta Arya, Akrati Saxena
Network modeling has been explored extensively by means of theoretical analysis as well as numerical simulations for Network Reconstruction (NR). The network reconstruction problem requires the estimation of the power-law exponent (γ) of a given input network. Thus, the effectiveness of the NR solution depends on the accuracy of the calculation of γ. In this article, we re-examine the degree distribution-based
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Supervised Clustering of Persian Handwritten Images Using Regularization and Dimension Reduction Methods ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-27 Sajedeh Moradnia, Mousa Golalizadeh
Clustering, as a fundamental exploratory data technique, not only is used to discover patterns and structures in complex datasets but also is utilized to group variables in high-dimensional data analysis. Dimension reduction through clustering helps identify important variables and reduce data dimensions without losing significant information. High-dimensional image datasets, such as Persian handwritten
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A Survey on AutoML Methods and Systems for Clustering ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-27 Yannis Poulakis, Christos Doulkeridis, Dimosthenis Kyriazis
Automated Machine Learning (AutoML) aims to identify the best-performing machine learning algorithm along with its input parameters for a given dataset and a specific machine learning task. This is a challenging problem, as the process of finding the best model and tuning it for a particular problem at hand is both time-consuming for a data scientist and computationally expensive. In this survey, we
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Fairness-Aware Graph Neural Networks: A Survey ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-24 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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BapFL : You can Backdoor Personalized Federated Learning ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-23 Tiandi Ye, Cen Chen, Yinggui Wang, Xiang Li, Ming Gao
In federated learning (FL), malicious clients could manipulate the predictions of the trained model through backdoor attacks, posing a significant threat to the security of FL systems. Existing research primarily focuses on backdoor attacks and defenses within the generic federated learning scenario, where all clients collaborate to train a single global model. A recent study conducted by Qin et al
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Citation Forecasting with Multi-Context Attention-Aided Dependency Modeling ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-23 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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Learning to Generate Temporal Origin-destination Flow Based on Urban Regional Features and Traffic Information ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-20 Can Rong, Zhicheng Liu, Jingtao Ding, Yong Li
Origin-destination (OD) flow contains population mobility information between every two regions in the city, which is of great value in urban planning and transportation management. Nevertheless, the collection of OD flow data is extremely difficult due to the hindrance of privacy issues and collection costs. Significant efforts have been made to generate OD flow based on urban regional features, e
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Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-20 Chen Zhao, Feng Mi, Xintao Wu, Kai Jiang, Latifur Khan, Feng Chen
The fairness-aware online learning framework has emerged as a potent tool within the context of continuous lifelong learning. In this scenario, the learner’s objective is to progressively acquire new tasks as they arrive over time, while also guaranteeing statistical parity among various protected sub-populations, such as race and gender, when it comes to the newly introduced tasks. A significant limitation
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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 : 2024-02-20 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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Dual-side Adversarial Learning based Fair Recommendation for Sensitive Attribute Filtering ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-19 Shenghao Liu, Yu Zhang, Lingzhi Yi, Xianjun Deng, Laurence T. Yang, Bang Wang
With the development of recommendation algorithms, researchers are paying increasing attention to fairness issues such as user discrimination in recommendations. To address these issues, existing works often filter users’ sensitive information that may cause discrimination during the process of learning user representations. However, these approaches overlook the latent relationship between items’
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PU-Detector: A PU Learning-based Framework for Real Money Trading Detection in MMORPG ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Yilin Wang, Sha Zhao, Shiwei Zhao, Runze Wu, Yuhong Xu, Jianrong Tao, Tangjie Lv, Shijian Li, Zhipeng Hu, Gang Pan
Massive multiplayer online role-playing games (MMORPG) have been becoming one of the most popular and exciting online games. In recent years, a cheating phenomenon called real money trading (RMT) has arisen and damaged the fantasy world in many ways. RMT is the sale of in-game items, currency, or even characters to earn real money, breaking the balance of the game economy ecosystem and damaging the
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HITS-based Propagation Paradigm for Graph Neural Networks ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Mehak Khan, Gustavo B. M. Mello, Laurence Habib, Paal Engelstad, Anis Yazidi
In this article, we present a new propagation paradigm based on the principle of Hyperlink-Induced Topic Search (HITS) algorithm. The HITS algorithm utilizes the concept of a “self-reinforcing” relationship of authority-hub. Using HITS, the centrality of nodes is determined via repeated updates of authority-hub scores that converge to a stationary distribution. Unlike PageRank-based propagation methods
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Do we really need imputation in AutoML predictive modeling? ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-16 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 AutoML setting, a question that naturally arises is to what extent sophisticated imputation algorithms
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Intricate Spatiotemporal Dependency Learning for Temporal Knowledge Graph Reasoning ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-16 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 the future facts, still faces with the dilemma due to the complex interactions between entities over time. This paper proposes a novel intricate Spatiotemporal Dependency learning
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Pre-training Question Embeddings for Improving Knowledge Tracing with Self-supervised Bi-graph Co-contrastive Learning ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-12 Wentao Wang, Huifang Ma, Yan Zhao, Zhixin Li
Learning high-quality vector representations (aka. embeddings) of educational questions lies at the core of knowledge tracing (KT), which defines a task of estimating students’ knowledge states by predicting the probability that they correctly answer questions. Although existing KT efforts have leveraged question information to achieve remarkable improvements, most of them learn question embeddings
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Finding Subgraphs with Maximum Total Density and Limited Overlap in Weighted Hypergraphs ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-12 Oana Balalau, Francesco Bonchi, T-H. Hubert Chan, Francesco Gullo, Mauro Sozio, Hao Xie
Finding dense subgraphs in large (hyper)graphs is a key primitive in a variety of real-world application domains, encompassing social network analytics, event detection, biology, and finance. In most such applications, one typically aims at finding several (possibly overlapping) dense subgraphs, which might correspond to communities in social networks or interesting events. While a large amount of
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On Breaking Truss-Based and Core-Based Communities ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-14 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 which 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
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Social Behavior Analysis in Exclusive Enterprise Social Networks by FastHAND ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-12 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 Twitter and Yammer, are ingeniously designed to facilitate unregulated communications among equal individuals. However
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Exploring the Learning Difficulty of Data: Theory and Measure ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Weiyao Zhu, Ou Wu, Fengguang Su, Yingjun Deng
‘‘Easy/hard sample” is a popular parlance in machine learning. Learning difficulty of samples refers to how easy/hard a sample is during a learning procedure. An increasing need of measuring learning difficulty demonstrates its importance in machine learning (e.g., difficulty-based weighting learning strategies). Previous literature has proposed a number of learning difficulty measures. However, no
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Rationalizing Graph Neural Networks with Data Augmentation ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Gang Liu, Eric Inae, Tengfei Luo, Meng Jiang
Graph rationales are representative subgraph structures that best explain and support the graph neural network (GNN) predictions. Graph rationalization involves the joint identification of these subgraphs during GNN training, resulting in improved interpretability and generalization. GNN is widely used for node-level tasks such as paper classification and graph-level tasks such as molecular property
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Swarm Self-supervised Hypergraph Embedding for Recommendation ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Meng Jian, Yulong Bai, Jingjing Guo, Lifang Wu
The information era brings both opportunities and challenges to information services. Confronting information overload, recommendation technology is dedicated to filtering personalized content to meet users’ requirements. The extremely sparse interaction records and their imbalanced distribution become a big obstacle to building a high-quality recommendation model. In this article, we propose a swarm
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Image Hash Layer Triggered CNN Framework for Wafer Map Failure Pattern Retrieval and Classification ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Minghao Piao, Yi Sheng, Jinda Yan, Cheng Hao Jin
Recently, deep learning methods are often used in wafer map failure pattern classification. CNN requires less feature engineering but still needs preprocessing, e.g., denoising and resizing. Denoising is used to improve the quality of the input data, and resizing is used to transform the input into an identical size when the input data sizes are various. However, denoising and resizing may distort
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Graph-based Text Classification by Contrastive Learning with Text-level Graph Augmentation ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Ximing Li, Bing Wang, Yang Wang, Meng Wang
Text Classification (TC) is a fundamental task in the information retrieval community. Nowadays, the mainstay TC methods are built on the deep neural networks, which can learn much more discriminative text features than the traditional shallow learning methods. Among existing deep TC methods, the ones based on Graph Neural Network (GNN) have attracted more attention due to the superior performance
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Cross-modal Multiple Granularity Interactive Fusion Network for Long Document Classification ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Tengfei Liu, Yongli Hu, Junbin Gao, Yanfeng Sun, Baocai Yin
Long Document Classification (LDC) has attracted great attention in Natural Language Processing and achieved considerable progress owing to the large-scale pre-trained language models. In spite of this, as a different problem from the traditional text classification, LDC is far from being settled. Long documents, such as news and articles, generally have more than thousands of words with complex structures
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Measuring and Mitigating Gender Bias in Legal Contextualized Language Models ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Mustafa Bozdag, Nurullah Sevim, Aykut Koç
Transformer-based contextualized language models constitute the state-of-the-art in several natural language processing (NLP) tasks and applications. Despite their utility, contextualized models can contain human-like social biases, as their training corpora generally consist of human-generated text. Evaluating and removing social biases in NLP models has been a major research endeavor. In parallel
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Totally-ordered Sequential Rules for Utility Maximization ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-12 Chunkai Zhang, Maohua Lyu, Wensheng Gan, Philip S. Yu
High-utility sequential pattern mining (HUSPM) is a significant and valuable activity in knowledge discovery and data analytics with many real-world applications. In some cases, HUSPM can not provide an excellent measure to predict what will happen. High-utility sequential rule mining (HUSRM) discovers high utility and high confidence sequential rules, so it can solve the issue in HUSPM. However, all
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A Lightweight, Effective, and Efficient Model for Label Aggregation in Crowdsourcing ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Yi Yang, Zhong-Qiu Zhao, Gongqing Wu, Xingrui Zhuo, Qing Liu, Quan Bai, Weihua Li
Due to the presence of noise in crowdsourced labels, label aggregation (LA) has become a standard procedure for post-processing these labels. LA methods estimate true labels from crowdsourced labels by modeling worker quality. However, most existing LA methods are iterative in nature. They require multiple passes through all crowdsourced labels, jointly and iteratively updating true labels and worker
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Robust Graph Meta-Learning for Weakly Supervised Few-Shot Node Classification ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Kaize Ding, Jianling Wang, Jundong Li, James Caverlee, Huan Liu
Graph machine learning (Graph ML) models typically require abundant labeled instances to provide sufficient supervision signals, which is commonly infeasible in real-world scenarios since labeled data for newly emerged concepts (e.g., new categorizations of nodes) on graphs is rather limited. To efficiently learn with a small amount of data on graphs, meta-learning has been investigated in Graph ML
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Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-12 Wei Chen, Chao Huang, Yanwei Yu, Yongguo Jiang, Junyu Dong
Trajectory-User Linking (TUL) is crucial for human mobility modeling by linking different trajectories to users with the exploration of complex mobility patterns. Existing works mainly rely on the recurrent neural framework to encode the temporal dependencies in trajectories, have fall short in capturing spatial-temporal global context for TUL prediction. To fill this gap, this work presents a new
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Learning Global and Multi-granularity Local Representation with MLP for Sequential Recommendation ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Chao Long, Huanhuan Yuan, Junhua Fang, Xuefeng Xian, Guanfeng Liu, Victor S. Sheng, Pengpeng Zhao
Sequential recommendation aims to predict the next item of interest to users based on their historical behavior data. Usually, users’ global and local preferences jointly affect the final recommendation result in different ways. Most existing works use transformers to globally model sequences, which makes them face the dilemma of quadratic computational complexity when dealing with long sequences.
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Distributional Learning for Network Alignment with Global Constraints ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-12 Hui Xu, Liyao Xiang, Xiaoying Gan, Luoyi Fu, Xinbing Wang, Chenghu Zhou
Network alignment, pairing corresponding nodes across the source and target networks, plays an important role in many data mining tasks. Extensive studies focus on learning node embeddings across different networks in a unified space. However, these methods have not taken the large structural discrepancy between aligned nodes into account and, thus, are largely confined by the deterministic representations
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Multiple-instance Learning from Triplet Comparison Bags ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-12 Senlin Shu, Deng-Bao Wang, Suqin Yuan, Hongxin Wei, Jiuchuan Jiang, Lei Feng, Min-Ling Zhang
Multiple-instance learning (MIL) solves the problem where training instances are grouped in bags, and a binary (positive or negative) label is provided for each bag. Most of the existing MIL studies need fully labeled bags for training an effective classifier, while it could be quite hard to collect such data in many real-world scenarios, due to the high cost of data labeling process. Fortunately,
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Diverse Structure-Aware Relation Representation in Cross-Lingual Entity Alignment ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-13 Yuhong Zhang, Jianqing Wu, Kui Yu, Xindong Wu
Cross-lingual entity alignment (CLEA) aims to find equivalent entity pairs between knowledge graphs (KGs) in different languages. It is an important way to connect heterogeneous KGs and facilitate knowledge completion. Existing methods have found that incorporating relations into entities can effectively improve KG representation and benefit entity alignment, and these methods learn relation representation
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Hi-PART: Going Beyond Graph Pooling with Hierarchical Partition Tree for Graph-Level Representation Learning ACM Trans. Knowl. Discov. Data (IF 3.6) Pub Date : 2024-02-12 Yuyang Ren, Haonan Zhang, Luoyi Fu, Shiyu Liang, Lei Zhou, Xinbing Wang, Xinde Cao, Fei Long, Chenghu Zhou
Graph pooling refers to the operation that maps a set of node representations into a compact form for graph-level representation learning. However, existing graph pooling methods are limited by the power of the Weisfeiler–Lehman (WL) test in the performance of graph discrimination. In addition, these methods often suffer from hard adaptability to hyper-parameters and training instability. To address