Link Prediction With Personalized Social Influence

Huo, Zepeng; Huang, Xiao; Hu, Xia

doi:10.1609/aaai.v32i1.11892

Cited by 17 publications

(6 citation statements)

References 16 publications

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“…In fact, in the field of smart grid, there is a strong spatial correlation and continuity between projects, which cannot be accurately analyzed and predicted using traditional machine learning algorithms. Some researches put forward non-linear neural network model to realize feature extraction and projects prediction of large amount of data [24][25][26]. Zhang et al [24] describe a method to predict the link in a network with GNN, but it only use the relationship in the network.…”

Section: Single Feature Extraction Modelmentioning

confidence: 99%

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MF-GCN-LSTM: a cloud-edge distributed framework for key positions prediction in grid projects

et al. 2022

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In this article, we solve the key positions prediction problem of engineering projects in smart grid, which pays more attention to the spatial-temporal distribution of projects. Many studies show that the projects are affected by multi-dimensional features such as time, space, correlation etc. However, few work can accurately predict the key positions of projects based on multi-dimensional features. In order to solve this problem, we propose the idea of multi-feature extraction, and make use of the real-world records trace to conduct multi-dimensional modeling. Then we introduce a multi-dimensional features extraction model: Multi-Feature-based GCN-LSTM (MF-GCN-LSTM) to take the effect of time, space and correlation for predicting the key positions of projects. Experiments on different datasets with various project types have proved that our model can complete the key positions prediction task efficiently. Compared with the other traditional method and non-linear models, our model shows higher prediction accuracy and robustness. Moreover, we show that the whole prediction framework MF-GCN-LSTM can be split and deployed in a distributed manner to accelerate the inference of the model under the cloud edge system.

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Section: Single Feature Extraction Modelmentioning

confidence: 99%

“…Zhang et al [24] describe a method to predict the link in a network with GNN, but it only use the relationship in the network. Huo et al [25] propose a method to predict the link in network with personalized social influence. Li et al [26] propose a method to generate security guaranteed image watermark.…”

Section: Single Feature Extraction Modelmentioning

confidence: 99%

MF-GCN-LSTM: a cloud-edge distributed framework for key positions prediction in grid projects

et al. 2022

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“…is mainly interested in OSN with Pinterest, but our approach is based on offline MSN. Additionally, researchers (Liben-Nowell and Kleinberg 2007;Huo, Huang, and Hu 2018;Trouillon et al 2016;Wang et al 2015) have made great efforts in link prediction in social network. For example, in (Liu et al 2016), a random walk principle is put forward to calculate the possibility of one node propagating information to another node.…”

Section: Related Workmentioning

confidence: 99%

D2D-LSTM: LSTM-Based Path Prediction of Content Diffusion Tree in Device-to-Device Social Networks

Zhang

Wang

Chen

et al. 2020

AAAI

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With the proliferation of mobile device users, the Device-to-Device (D2D) communication has ascended to the spotlight in social network for users to share and exchange enormous data. Different from classic online social network (OSN) like Twitter and Facebook, each single data file to be shared in the D2D social network is often very large in data size, e.g., video, image or document. Sometimes, a small number of interesting data files may dominate the network traffic, and lead to heavy network congestion. To reduce the traffic congestion and design effective caching strategy, it is highly desirable to investigate how the data files are propagated in offline D2D social network and derive the diffusion model that fits to the new form of social network. However, existing works mainly concern about link prediction, which cannot predict the overall diffusion path when network topology is unknown. In this article, we propose D2D-LSTM based on Long Short-Term Memory (LSTM), which aims to predict complete content propagation paths in D2D social network. Taking the current user's time, geography and category preference into account, historical features of the previous path can be captured as well. It utilizes prototype users for prediction so as to achieve a better generalization ability. To the best of our knowledge, it is the first attempt to use real world large-scale dataset of mobile social network (MSN) to predict propagation path trees in a top-down order. Experimental results corroborate that the proposed algorithm can achieve superior prediction performance than state-of-the-art approaches. Furthermore, D2D-LSTM can achieve 95% average precision for terminal class and 17% accuracy for tree path hit.

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“…Graphs are ubiquitous data structures in modeling the interactions between objects. As a fundamental task in graph data mining, link prediction has been found in many real-world applications, such as social media analysis [15], drug discovery [1], and recommender systems [9]. However, data in these applications is often dynamic.…”

Section: Introductionmentioning

confidence: 99%

Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs

Luo,

Haffari,

Pan

2022

Preprint

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Link prediction on dynamic graphs is an important task in graph mining. Existing approaches based on dynamic graph neural networks (DGNNs) typically require a significant amount of historical data (interactions over time), which is not always available in practice. The missing links over time, which is a common phenomenon in graph data, further aggravates the issue and thus creates extremely sparse and dynamic graphs. To address this problem, we propose a novel method based on the neural process, called Graph Sequential Neural ODE Process (GSNOP). Specifically, GSNOP combines the advantage of the neural process and neural ordinary differential equation (ODE) that models the link prediction on dynamic graphs as a dynamic-changing stochastic process. By defining a distribution over functions, GSNOP introduces the uncertainty into the predictions, making it generalize to more situations instead of overfitting to the sparse data. GSNOP is also agnostic to model structures that can be integrated with any DGNN to consider the chronological and geometrical information for link prediction. Extensive experiments on three dynamic graph datasets show that GSNOP can significantly improve the performance of existing DGNNs and outperform other neural process variants. CCS CONCEPTS• Networks → Network algorithms; • Computing methodologies → Machine learning.

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Link Prediction With Personalized Social Influence

Cited by 17 publications

References 16 publications

MF-GCN-LSTM: a cloud-edge distributed framework for key positions prediction in grid projects

MF-GCN-LSTM: a cloud-edge distributed framework for key positions prediction in grid projects

D2D-LSTM: LSTM-Based Path Prediction of Content Diffusion Tree in Device-to-Device Social Networks

Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs

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