Deep Representation Learning for Social Network Analysis

Tan, Qiaoyu; Liu, Ninghao; Hu, Xia

doi:10.3389/fdata.2019.00002

Cited by 79 publications

(32 citation statements)

References 70 publications

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“…Graph representation learning. This line of work has been studied and analyzed widely in social network analysis and recommendation scenarios [17,45,57]. Representatives including models based on matrix factorization [32,40,47], random walks [8,15,39] or deep learning [4,49,50].…”

Section: Related Workmentioning

confidence: 99%

Learning to Hash with Graph Neural Networks for Recommender Systems

Tan

Liu

Zhao

et al. 2020

Proceedings of the Web Conference 2020

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Recommender systems in industry generally include two stages: recall and ranking. Recall refers to efficiently identify hundreds of candidate items that user may interest in from a large volume of item corpus, while the latter aims to output a precise ranking list using complex ranking models. Recently, graph representation learning has attracted much attention in supporting high quality candidate search at scale. Despite its effectiveness in learning embedding vectors for objects in the user-item interaction network, the computational costs to infer users' preferences in continuous embedding space are tremendous. In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes. Specifically, a deep hashing with GNNs (HashGNN) is presented, which consists of two components, a GNN encoder for learning node representations, and a hash layer for encoding representations to hash codes. The whole architecture is trained end-to-end by jointly optimizing two losses, i.e., reconstruction loss from reconstructing observed links, and ranking loss from preserving the relative ordering of hash codes. A novel discrete optimization strategy based on straight through estimator (STE) with guidance is proposed. The principal idea is to avoid gradient magnification in back-propagation of STE with continuous embedding guidance, in which we begin from learning an easier network that mimic the continuous embedding and let it evolve during the training until it finally goes back to STE. Comprehensive experiments over three publicly available and one real-world Alibaba company datasets demonstrate that our model not only can achieve comparable performance compared with its continuous counterpart but also runs multiple times faster during inference.

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Section: Related Workmentioning

confidence: 99%

Learning to Hash with Graph Neural Networks for Recommender Systems

Tan

Liu

Zhao

et al. 2020

Proceedings of the Web Conference 2020

Self Cite

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“…Deep learning has outstanding performance in various research fields, including related research based on social networks [ 18 ]. With Facebook, Twitter, and other social platforms becoming an integral part of people’s daily life, their information has become efficient data sources for researchers to study social networks using deep learning methods [ 18 , 19 , 20 ]. Through the analysis of the social network data, researchers can summarize the interests and views of each user (node), find the relationship characteristics from the communication (link) between users (nodes), and study the communication of public events in social media platforms.…”

Section: Related Workmentioning

confidence: 99%

Topic-Emotion Propagation Mechanism of Public Emergencies in Social Networks

Cai

Luo

Xiao

et al. 2021

Sensors

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The information propagation of emergencies in social networks is often accompanied by the dissemination of the topic and emotion. As a virtual sensor of public emergencies, social networks have been widely used in data mining, knowledge discovery, and machine learning. From the perspective of network, this study aims to explore the topic and emotion propagation mechanism, as well as the interaction and communication relations of the public in social networks under four types of emergencies, including public health events, accidents and disasters, social security events, and natural disasters. Event topics were identified by Word2vec and K-means clustering. The biLSTM model was used to identify emotion in posts. The propagation maps of topic and emotion were presented visually on the network, and the synergistic relationship between topic and emotion propagation as well as the communication characteristics of multiple subjects were analyzed. The results show that there were similarities and differences in the propagation mechanism of topic and emotion in different types of emergencies. There was a positive correlation between topic and emotion of different types of users in social networks in emergencies. Users with a high level of topic influence were often accompanied by a high level of emotion appeal.

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“…Currently, there are widespread applications of deep learning (DL) around the world. These applications include health care [ 1 ], visual data processing [ 2 ], social network analysis [ 3 ], and audio and speech processing (e.g., recognition and enhancement) [ 4 ]. The efficacy of DL models such as reinforcement learning [ 5 ], long short-term memory [ 6 ] and auto-encoders [ 7 ] for solving dimensionality problems has been proven.…”

Section: Introductionmentioning

confidence: 99%

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

151

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Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

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Deep Representation Learning for Social Network Analysis

Cited by 79 publications

References 70 publications

Learning to Hash with Graph Neural Networks for Recommender Systems

Learning to Hash with Graph Neural Networks for Recommender Systems

Topic-Emotion Propagation Mechanism of Public Emergencies in Social Networks

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

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