A survey on semi-supervised graph clustering

Daneshfar, Fatemeh; Soleymanbaigi, Sayvan; Yamini, Pedram; Amini, Mohammad Sadra

doi:10.1016/j.engappai.2024.108215

Cited by 9 publications

(2 citation statements)

References 129 publications

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“…In our future research endeavors, we aim to broaden our focus beyond solely self-supervised learning of graphs. Instead, we intend to explore a wider array of topics, including the semi-supervised learning of graphs [45], the security of graph data [46], and privacy concerns in graph recommendation systems [47]. This expansion will enable our recommendation systems to better align with real-world applications and offer numerous intriguing avenues for future exploration.…”

Section: Discussionmentioning

confidence: 99%

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

Duan,

Wang,

Zhong

2024

Mathematics

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As user–item interaction information is typically limited, collaborative filtering (CF)-based recommender systems often suffer from the data sparsity issue. To address this issue, recent recommender systems have turned to graph neural networks (GNNs) due to their superior performance in capturing high-order relationships. Furthermore, some of these GNN-based recommendation models also attempt to incorporate other information. They either extract self-supervised signals to mitigate the data sparsity problem or employ social information to assist with learning better representations under a social recommendation setting. However, only a few methods can take full advantage of these different aspects of information. Based on some testing, we believe most of these methods are complex and redundantly designed, which may lead to sub-optimal results. In this paper, we propose SSGCL, which is a recommendation system model that utilizes both social information and self-supervised information. We design a GNN-based propagation strategy that integrates social information with interest information in a simple yet effective way to learn user–item representations for recommendations. In addition, a specially designed contrastive learning module is employed to take advantage of the self-supervised signals for a better user–item representation distribution. The contrastive learning module is jointly optimized with the recommendation module to benefit the final recommendation result. Experiments on several benchmark data sets demonstrate the significant improvement in performance achieved by our model when compared with baseline models.

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Section: Discussionmentioning

confidence: 99%

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

Duan,

Wang,

Zhong

2024

Mathematics

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“…This method combines temporal information and consistency regularization to improve the robustness of video object detection models, making it a valuable contribution to the field. Moreover, a survey on semisupervised graph clustering [34] provided an in-depth analysis of graph-based clustering methods under semi-supervised settings, highlighting the advancements and applications in various domains. This survey underscores the versatility of semi-supervised learning in different contexts and its impact on improving clustering performance.…”

Section: Related Workmentioning

confidence: 99%

Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4

Gomaa,

Abdalrazik

2024

WEVJ

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Moving object detection is a vital research area that plays an essential role in intelligent transportation systems (ITSs) and various applications in computer vision. Recently, researchers have utilized convolutional neural networks (CNNs) to develop new techniques in object detection and recognition. However, with the increasing number of machine learning strategies used for object detection, there has been a growing need for large datasets with accurate ground truth used for the training, usually demanding their manual labeling. Moreover, most of these deep strategies are supervised and only applicable for specific scenes with large computational resources needed. Alternatively, other object detection techniques such as classical background subtraction need low computational resources and can be used with general scenes. In this paper, we propose a new a reliable semi-automatic method that combines a modified version of the detection-based CNN You Only Look Once V4 (YOLOv4) technique and background subtraction technique to perform an unsupervised object detection for surveillance videos. In this proposed strategy, background subtraction-based low-rank decomposition is applied firstly to extract the moving objects. Then, a clustering method is adopted to refine the background subtraction (BS) result. Finally, the refined results are used to fine-tune the modified YOLO v4 before using it in the detection and classification of objects. The main contribution of this work is a new detection framework that overcomes manual labeling and creates an automatic labeler that can replace manual labeling using motion information to supply labeled training data (background and foreground) directly from the detection video. Extensive experiments using real-world object monitoring benchmarks indicate that the suggested framework obtains a considerable increase in mAP compared to state-of-the-art results on both the CDnet 2014 and UA-DETRAC datasets.

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A faster deep graph clustering network based on dynamic graph weight update mechanism

2024

Cluster Comput

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A survey on semi-supervised graph clustering

Cited by 9 publications

References 129 publications

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

SSGCL: Simple Social Recommendation with Graph Contrastive Learning

Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4

A faster deep graph clustering network based on dynamic graph weight update mechanism

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