POI neural-rec model via graph embedding representation

Yang, Kang; Zhu, Jinghua; Guo, Xu

doi:10.26599/tst.2019.9010059

Cited by 25 publications

(15 citation statements)

References 26 publications

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“…Not only considering graph structures, GAE [16] uses Graph Convolutional Networks (GCNs) to encode nodes in a graph and improves performance by incorporating node features. Further works [17,18] prove that GAEs can be successfully used for link prediction and recommendation systems. Although these prior graph embedding methods have been making good progress in different fields, less consideration is given to the privacy issue in graph embedding publishing.…”

Section: Graph Embedding Methodsmentioning

confidence: 99%

Link-privacy preserving graph embedding data publication with adversarial learning

Zhang

Tian

Cai

et al. 2022

Tsinghua Sci. Technol.

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The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods, aiming at learning a continuous vector space for the graph, which is amenable to be adopted in traditional machine learning algorithms in favor of vector representations. Graph embedding methods build an important bridge between social network analysis and data analytics, as social networks naturally generate an unprecedented volume of graph data continuously. Publishing social network data not only brings benefit for public health, disaster response, commercial promotion, and many other applications, but also gives birth to threats that jeopardize each individual's privacy and security. Unfortunately, most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social networks. To be specific, attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network embedding. In this paper, we propose a novel link-privacy preserved graph embedding framework using adversarial learning, which can reduce adversary's prediction accuracy on sensitive links, while persevering sufficient non-sensitive information, such as graph topology and node attributes in graph embedding. Extensive experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

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Section: Graph Embedding Methodsmentioning

confidence: 99%

Link-privacy preserving graph embedding data publication with adversarial learning

Zhang

Tian

Cai

et al. 2022

Tsinghua Sci. Technol.

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“…Deep learning-based epidemic control: Historical insights from temporal infection data have been crucial for epidemic control and prevention, and could benefit other problems in smart city systems [13,14] or enhanced social network analysis [15] . Deep learningbased techniques have demonstrated a remarkable performance to model such temporal correlations and recognize multiple patterns [16,17] , including the deep neural network-based short-term and high-resolution epidemic forecasting for influenza-like illness [18] , the semi-supervised deep learning framework that integrates computational epidemiology and social media mining techniques for epidemic simulation, called SimNest [19] and EpiRP [20] , which use representational learning methods to capture the dynamic characteristics of epidemic spreading on social networks for epidemicsoriented clustering and classification.…”

Section: Related Workmentioning

confidence: 99%

Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

Pang

Huang

Xie

et al. 2021

Tsinghua Sci. Technol.

136

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The novel coronavirus, COVID-19, has caused a crisis that affects all segments of the population. As the knowledge and understanding of COVID-19 evolve, an appropriate response plan for this pandemic is considered one of the most effective methods for controlling the spread of the virus. Recent studies indicate that a city Digital Twin (DT) is beneficial for tackling this health crisis, because it can construct a virtual replica to simulate factors, such as climate conditions, response policies, and people's trajectories, to help plan efficient and inclusive decisions.However, a city DTsystem relies on long-term and high-quality data collection to make appropriate decisions, limiting its advantages when facing urgent crises, such as the COVID-19 pandemic. Federated Learning (FL), in which all clients can learn a shared model while retaining all training data locally, emerges as a promising solution for accumulating the insights from multiple data sources efficiently. Furthermore, the enhanced privacy protection settings removing the privacy barriers lie in this collaboration. In this work, we propose a framework that fused city DT with FL to achieve a novel collaborative paradigm that allows multiple city DTs to share the local strategy and status quickly. In particular, an FL central server manages the local updates of multiple collaborators (city DTs), providing a global model that is trained in multiple iterations at different city DT systems until the model gains the correlations between various response plans and infection trends. This approach means a collaborative city DT paradigm fused with FL techniques can obtain knowledge and patterns from multiple DTs and eventually establish a "global view" of city crisis management. Meanwhile, it also helps improve each city's DT by consolidating other DT's data without violating privacy rules. In this paper, we use the COVID-19 pandemic as the use case of the proposed framework. The experimental results on a real dataset with various response plans validate our proposed solution and demonstrate its superior performance.

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“…Since the POI recommendation task is restricted by physical distance in real life, current research usually focuses on analyzing the influence of location factors caused by spatial information [11][12][13][14]. At present, many algorithms at home and abroad have studied the POI recommendation task.…”

Section: Related Researchmentioning

confidence: 99%

POI recommendation method using LSTM-attention in LBSN considering privacy protection

Wang

Lü

2021

Complex Intell. Syst.

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Aiming at the problems of traditional point of interest (POI), such as sparse data, lack of negative feedback, and dynamic and periodic changes of user preferences, a POI recommendation method using deep learning in location-based social networks (LBSN) considering privacy protection is proposed. First, the idea of Embedding is used to quantify the user information, friend relationship, POI information, and so on, so as to obtain the internal relationship of the location. Then, based on the user's history and current POI check-in sequence set, the long- and short-term attention mechanism (LSA) is constructed, and the quantified information is used as the input of LSA to better capture the user's long-term and short-term preferences. Finally, the social network information and semantic information are fitted in different input layers, and the time and geographical location information of user's historical behavior are used to recommend the next POI for users. Gowalla and Brightkite datasets are used to demonstrate the proposed method. The results show that the performance of the proposed method is better than other comparison methods under different sparsity, location sequence length, and embedding length. When the number of iterations is 500, the recommended method tends to be stable, and the accuracy is 0.27. Moreover, the recommendation time of the proposed method is less than 130 ms, which is better than other comparative deep learning methods.

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POI neural-rec model via graph embedding representation

Cited by 25 publications

References 26 publications

Link-privacy preserving graph embedding data publication with adversarial learning

Link-privacy preserving graph embedding data publication with adversarial learning

Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

POI recommendation method using LSTM-attention in LBSN considering privacy protection

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