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IEEE Transactions on Knowledge and Data Engineering
Scopus
ISSN 1041-4347 · IEEE Computer Society · 21 artikel di indeks
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Artikel Jurnal Teks lengkap
Vertical Federated Learning: Concepts, Advances, and Challenges
Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models without exposing their raw data or model parameters. Motivated by the rapid growth in VFL research and real-world…
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Recommender Systems in the Era of Large Language Models (LLMs)
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an indispensable and important component in our daily lives, providing personalized suggestions that cater to user preferences. While Deep Neural Networks (DNNs) have achieved significant advancements…
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A Comprehensive Survey on Multi-View Clustering
The development of information gathering and extraction technology has led to the popularity of multi-view data, which enables samples to be seen from numerous perspectives. Multi-view clustering (MVC), which groups data samples by leveraging complementary and consensual information from several…
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Fast Multi-View Clustering Via Ensembles: Towards Scalability, Superiority, and Simplicity
Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restricting their feasibility for large-scale datasets. Second, they typically fuse multi-view information via one-stage…
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Deep Isolation Forest for Anomaly Detection
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard…
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A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which…
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ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
Outlier detection refers to the identification of data points that deviate from a general data distribution. Existing unsupervised approaches often suffer from high computational cost, complex hyperparameter tuning, and limited interpretability, especially when working with large, high-dimensional…
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Adversarial Attack and Defense on Graph Data: A Survey
Deep neural networks (DNNs) have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Though there are several works about…
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Multi-Modal Knowledge Graph Construction and Application: A Survey
Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs. However, most of existing knowledge graphs are represented with pure symbols, which hurts the machine's capability to understand the real world. The multi-modalization of…
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A Survey on Deep Semi-Supervised Learning
Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in deep semi-supervised learning methods from perspectives of model design and unsupervised loss functions. We first…
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Graph Self-Supervised Learning: A Survey
Deep learning on graphs has attracted significant interests recently. However, most of the works have focused on (semi-) supervised learning, resulting in shortcomings including heavy label reliance, poor generalization, and weak robustness. To address these issues, self-supervised learning (SSL)…
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Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering
Multi-view clustering is an important research topic due to its capability to utilize complementary information from multiple views. However, there are few methods to consider the negative impact caused by certain views with unclear clustering structures, resulting in poor multi-view clustering…
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Challenges in KNN Classification
The KNN algorithm is one of the most popular data mining algorithms. It has been widely and successfully applied to data analysis applications across a variety of research topics in computer science. This paper illustrates that, despite its success, there remain many challenges in KNN…
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A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
As data privacy increasingly becomes a critical societal concern, federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning…
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Self-supervised Learning: Generative or Contrastive
Deep supervised learning has achieved great success in the last decade. However, its defects of heavy dependence on manual labels and vulnerability to attacks have driven people to find other paradigms. As an alternative, self-supervised learning (SSL) attracts many researchers for its soaring…
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A Comprehensive Survey on Graph Anomaly Detection With Deep Learning
Anomalies are rare observations (e.g., data records or events) that deviate significantly from the others in the sample. Over the past few decades, research on anomaly mining has received increasing interests due to the implications of these occurrences in a wide range of disciplines - for…
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A General Survey on Attention Mechanisms in Deep Learning
Attention is an important mechanism that can be employed for a variety of deep learning models across many different domains and tasks. This survey provides an overview of the most important attention mechanisms proposed in the literature. The various attention mechanisms are explained by means of…
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Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems
Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a…
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Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detection
Smart contract vulnerability detection draws extensive attention in recent years due to the substantial losses caused by hacker-attacks. Existing efforts for contract security analysis heavily rely on rigid rules defined by experts, which is labor-intensive and non-scalable. More importantly…
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Heterogeneous Network Representation Learning: A Unified Framework With Survey and Benchmark
Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs). Meanwhile, representation learning (a.k.a.embedding) has recently…