Deep Learning for Community Detection: Progress, Challenges and Opportunities

Liu, Fanzhen; Xue, Shan; Wu, Jia; Zhou, Chuan; Hu, Wenbin; Paris, Cécile; Nepal, Surya; Yang, Jian; Yu, Philip S.

doi:10.24963/ijcai.2020/693

“…It also proved that GCN aggregation is a first-order approximation of spectral graph convolutions. Graph neural network also plays an important part in many areas like community detection [23,24] and recommender systems [7]. GNN-based SRS Motivated by the recent progress that graph convolution network achieved, researchers of recommender system shift their sight to this new territory.…”

Section: Graph Neural Networkmentioning

confidence: 99%

Item trend learning for sequential recommendation system using gated graph neural network

Tao

¹

,

Wang

²

,

Yao

³

et al. 2021

Neural Comput & Applic

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Recommendation system, or recommender system, is widely used in online Web applications like e-commerce Web sites and movie review Web sites. Sequential recommender put more emphasis upon user's short-term preference through exploiting information from its recent history. By incorporating the user short-term preference into the recommendation, the algorithm achieves a higher accuracy, which proves that a more accurate user portrait or representation boosts the performance to a great extent. Intuitionally, we seek to improve the current item representation modeling via incorporating the item trend information. Most of the recommendation models neglect the importance of the ever-changing item popularity. To this end, this paper introduces a novel sequential recommendation approach dubbed TRec. TRec learns the item trend information from the implicit user interaction history and incorporates the item trend information into the subsequent item recommendation tasks. After that, a self-attention mechanism is used for better representation. We also investigate alternative ways to model the proposed item trend representation; we evaluate two variant models which leverage the power of gated graph neural network upon the item trend representation modeling to boost the representation ability. We conduct extensive experiments with seven baseline methods on four benchmark datasets. The empirical results show that our proposed approach outperforms the state-of-the-art models as high as 18.2%. The experiment result displays the effectiveness in item trend information learning while with low computational complexity as well. Our study demonstrates the importance of item trend information in recommendation system.

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“…We can assure that y (0) � n i�0 c i x i and y (k) can be expressed as y (k) � n i�0 c i λ k i x i . Here, [y (0) , y (1) , . .…”

Section: Directed Path-based Node Importance Centralitymentioning

confidence: 99%

“…Analysis and explaining the dynamics and properties of social networks has become an interesting researching task with plenty of applications in social sciences and many other web application scripts. In some social networks, it is very common for some users who decide to leave the network or begin to stop being active in the activities of their community [1]. is phenomenon is also called as quitting or churn and has absorbed much research attention in social networks.…”

Section: Introductionmentioning

confidence: 99%

Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network

Deng

¹

,

Ding

²

,

Chen³

et al. 2020

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In recent years, while extensive researches on various networks properties have been proposed and accomplished, little has been proposed and done on network robustness and node vulnerability assessment under cascades in directed large-scale online community networks. In essential, an online directed social network is a group-centered and information spread-dominated online platform which is very different from the traditional undirected social network. Some further research studies have indicated that the online social network has high robustness to random removals of nodes but fails to the intentional attacks, particularly to those attacks based on node betweenness or node directed coefficient. To explore on the robustness of directed social network, in this article, we have proposed two novel node centralities of ITG (information transfer gain-based probability clustering coefficient) and I M p v (directed path-based node importance centrality). These two new centrality models are designed to capture this cascading effect in directed online social networks. Furthermore, we also propose a new and highly efficient computing method based on iterations for I M p v . Then, with the abundant experiments on the synthetic signed network and real-life networks derived from directed online social media and directed human mobile phone calling network, it has been proved that our ITG and I M p v based on directed social network robustness and node vulnerability assessment method is more accurate, efficient, and faster than several traditional centrality methods such as degree and betweenness. And we also have proposed the solid reasoning and proof process of iteration times k in computation of I M p v . To the best knowledge of us, our research has drawn some new light on the leading edge of robustness on the directed social network.

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“…Recently, machine learning techniques have achieved great success and been widely applied to various research fields [26][27][28]. One of the advantages of applying machine learning to quality evaluation is that it can directly take original image data as input and then combine feature learning with quality regression in the training procedure [29,30].…”

Section: Introductionmentioning

confidence: 99%

No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

Xu

¹

,

Guo²,

Chen

³

et al. 2021

Complexity

1

0

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Learning a deep structure representation for complex information networks is a vital research area, and assessing the quality of stereoscopic images or videos is challenging due to complex 3D quality factors. In this paper, we explore how to extract effective features to enhance the prediction accuracy of perceptual quality assessment. Inspired by the structure representation of the human visual system and the machine learning technique, we propose a no-reference quality assessment scheme for stereoscopic images. More specifically, the statistical features of the gradient magnitude and Laplacian of Gaussian responses are extracted to form binocular quality-predictive features. After feature extraction, these features of distorted stereoscopic image and its human perceptual score are used to construct a statistical regression model with the machine learning technique. Experimental results on the benchmark databases show that the proposed model generates image quality prediction well correlated with the human visual perception and delivers highly competitive performance with the typical and representative methods. The proposed scheme can be further applied to the real-world applications on video broadcasting and 3D multimedia industry.

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Deep Learning for Community Detection: Progress, Challenges and Opportunities

Cited by 192 publications

References 30 publications

Item trend learning for sequential recommendation system using gated graph neural network

Item trend learning for sequential recommendation system using gated graph neural network

Novel Node Centrality-Based Efficient Empirical Robustness Assessment for Directed Network

No-Reference Stereoscopic Image Quality Assessment Based on Binocular Statistical Features and Machine Learning

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