ACM Transactions on Knowledge Discovery from Data

Recommendation systems powered by artificial intelligence (AI) are widely used to improve user experience. However, AI inevitably raises privacy leakage and other security issues due to the utilization of extensive user data. Addressing these challenges ...

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together with representation collapse, largely limits the performance of the GNNs in ...

Fair feature selection for classification decision tasks has recently garnered significant attention from researchers. However, existing fair feature selection algorithms fall short of providing a full explanation of the causal relationship between ...

Graphs can facilitate modeling various complex systems such as gene networks and power grids as well as analyzing the underlying relations within them. Learning over graphs has recently attracted increasing attention, particularly graph neural network (...

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 ...

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 ...

Currently, the medical field is witnessing an increase in the use of machine learning techniques. Supervised learning methods adopted in classification, prediction, and segmentation tasks for medical images always experience decreased performance when the ...

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 ...

Since social media such as Facebook and X (formerly known as 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 ...

Society is increasingly relying on predictive models in fields like criminal justice, credit risk management, and hiring. To prevent such automated systems from discriminating against people belonging to certain groups, fairness measures have become a ...

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 ...

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  ...

As AI systems have obtained significant performance to be deployed widely in our daily lives 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 ...

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 ...

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 ...

Federated Learning (FL) provides 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)...

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 ...

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 ...

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 ...

In this article, 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 ...

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 ...

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. These ...