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- research-articleSeptember 2024JUST ACCEPTED
Exploiting Pre-trained Language Models for Black-box Attack against Knowledge Graph Embeddings
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3688850Despite the emerging research on adversarial attacks against Knowledge Graph Embedding (KGE) models, most of them focus on white-box attack settings. However, white-box attacks are difficult to apply in practice compared to black-box attacks since they ...
- research-articleAugust 2024JUST ACCEPTED
Causal Discovery Using Weight-Based Conditional Independence Test
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3687467Conditional independence (CI) tests play an essential role in causal discovery from observational data, enabling the measurement of independence between two nodes. However, traditional CI tests ignore the imbalanced occurrence probabilities of node values,...
- research-articleAugust 2024JUST ACCEPTED
Heterogeneous Network Motif Coding, Counting, and Profiling
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3687465Network motifs, as a fundamental higher-order structure in large-scale networks, have received significant attention over recent years. Particularly in heterogeneous networks, motifs offer a higher capacity to uncover diverse information compared to ...
- research-articleAugust 2024JUST ACCEPTED
ProcessGAN: Generating Privacy-Preserving Time-Aware Process Data with Conditional Generative Adversarial Nets
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3687464Process data constructed from event logs provides valuable insights into procedural dynamics over time. The confidential information in process data, together with the data's intricate nature, makes the datasets not sharable and challenging to collect. ...
- research-articleAugust 2024JUST ACCEPTED
Towards Cross-lingual Social Event Detection with Hybrid Knowledge Distillation
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3689948Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the common lesser-spoken ...
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- research-articleAugust 2024
Fair Federated Learning with Multi-Objective Hyperparameter Optimization
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 8Article No.: 208, Pages 1–13https://doi.org/10.1145/3676968Federated learning (FL) is an attractive paradigm for privacy-aware distributed machine learning, which enables clients to collaboratively learn a global model without sharing clients’ data. Recently, many strategies have been proposed to improve the ...
- research-articleAugust 2024
Neighbor-Enhanced Representation Learning for Link Prediction in Dynamic Heterogeneous Attributed Networks
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 8Article No.: 204, Pages 1–25https://doi.org/10.1145/3676559Dynamic link prediction aims to predict future connections among unconnected nodes in a network. It can be applied for friend recommendations, link completion, and other tasks. Network representation learning algorithms have demonstrated considerable ...
- research-articleAugust 2024
Deconfounding User Preference in Recommendation Systems through Implicit and Explicit Feedback
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 8Article No.: 198, Pages 1–18https://doi.org/10.1145/3673762Recommender systems are influenced by many confounding factors (i.e., confounders) which result in various biases (e.g., popularity biases) and inaccurate user preference. Existing approaches try to eliminate these biases by inference with causal graphs. ...
- research-articleAugust 2024
Learning Individual Treatment Effects under Heterogeneous Interference in Networks
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 8Article No.: 199, Pages 1–21https://doi.org/10.1145/3673761Estimating individual treatment effects in networked observational data is a crucial and increasingly recognized problem. One major challenge of this problem is violating the stable unit treatment value assumption (SUTVA), which posits that a unit’s ...
- research-articleAugust 2024
Structure-Information-Based Reasoning over the Knowledge Graph: A Survey of Methods and Applications
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 8Article No.: 210, Pages 1–42https://doi.org/10.1145/3671148The knowledge graph (KG) is an efficient form of knowledge organization and expression, providing prior knowledge support for various downstream tasks, and has received extensive attention in natural language processing. However, existing large-scale KGs ...
- research-articleAugust 2024JUST ACCEPTED
Neural-Symbolic Methods for Knowledge Graph Reasoning: A Survey
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3686806Neural symbolic knowledge graph (KG) reasoning offers a promising approach that combines the expressive power of symbolic reasoning with the learning capabilities inherent in neural networks. This survey provides a comprehensive overview of advancements, ...
- research-articleAugust 2024JUST ACCEPTED
Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Trustworthy Response Generation in Chinese
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3686807Large Language Models (LLMs) have demonstrated remarkable success in diverse natural language processing (NLP) tasks in general domains. However, LLMs sometimes generate responses with the hallucination about medical facts due to limited domain knowledge. ...
- research-articleAugust 2024JUST ACCEPTED
Billiards Sports Analytics: Datasets and Tasks
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3686804Nowadays, it becomes a common practice to capture some data of sports games with devices such as GPS sensors and cameras and then use the data to perform various analyses on sports games, including tactics discovery, similar game retrieval, performance ...
- research-articleJuly 2024JUST ACCEPTED
Fair-RGNN: Mitigating Relational Bias on Knowledge Graphs
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3681792Knowledge graph data are prevalent in real-world applications, and knowledge graph neural networks (KGNNs) are essential techniques for knowledge graph representation learning. Although KGNN effectively models the structural information from knowledge ...
- research-articleJuly 2024
Anomaly Detection in Dynamic Graphs: A Comprehensive Survey
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 8Article No.: 192, Pages 1–44https://doi.org/10.1145/3669906This survey article presents a comprehensive and conceptual overview of anomaly detection (AD) using dynamic graphs. We focus on existing graph-based AD techniques and their applications to dynamic networks. The contributions of this survey article ...
- research-articleJuly 2024
FastHGNN: A New Sampling Technique for Learning with Hypergraph Neural Networks
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 8Article No.: 184, Pages 1–26https://doi.org/10.1145/3663670Hypergraphs can represent higher-order relations among objects. Traditional hypergraph neural networks involve node-edge-node transform, leading to high computational cost and timing. The main aim of this article is to propose a new sampling technique for ...
- research-articleJuly 2024
Variate Associated Domain Adaptation for Unsupervised Multivariate Time Series Anomaly Detection
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 8Article No.: 187, Pages 1–24https://doi.org/10.1145/3663573Multivariate Time Series Anomaly Detection (MTS-AD) is crucial for the effective management and maintenance of devices in complex systems, such as server clusters, spacecrafts, and financial systems, and so on. However, upgrade or cross-platform ...
- research-articleJuly 2024JUST ACCEPTED
Exploiting Pre-Trained Models and Low-Frequency Preference for Cost-Effective Transfer-based Attack
ACM Transactions on Knowledge Discovery from Data (TKDD), Just Accepted https://doi.org/10.1145/3680553The transferability of adversarial examples enables practical transfer-based attacks. However, existing theoretical analysis cannot effectively reveal what factors contribute to cross-model transferability. Furthermore, the assumption that the target ...