Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks

Ge, Junbo; Shi, Leilei; Liu, Lu; Si, Hongwei

doi:10.1155/2021/3860083

Cited by 8 publications

(5 citation statements)

References 31 publications

(59 reference statements)

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“…Therefore, Ref. [28] incorporated node features into community detection algorithms based on the characteristics of social networks, further improving the process of community discovery methods in link prediction tasks. Based on rule-based methods, the Anytime Bottom-Up Rule Learning (AnyBURL) [29] model performed well in various general link prediction tasks.…”

Section: Link Prediction Methodsmentioning

confidence: 99%

Sparse Subgraph Prediction Based on Adaptive Attention

Gao

et al. 2023

Applied Sciences

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Link prediction is a crucial problem in the analysis of graph-structured data, and graph neural networks (GNNs) have proven to be effective in addressing this problem. However, the computational and temporal costs associated with large-scale graphs remain a concern. This study introduces a novel method for link prediction called Sparse Subgraph Prediction Based on Adaptive Attention (SSP-AA). The method generates sparse subgraphs and utilizes Graph SAmple and aggreGatE (GraphSAGE) for prediction, aiming to reduce computation and time costs while providing a foundation for future exploration of large-scale graphs. Certain key issues in GraphSAGE are addressed by integrating an adaptive attention mechanism and a jumping knowledge module into the model. To address the issue of adaptive weight distribution in GraphSAGE, an aggregation function is employed, which is based on the attention mechanism. This modification enables the model to distribute weights adaptively among neighboring nodes, significantly improving its ability to capture node relationships. Furthermore, to tackle the common issue of over-smoothing in GNNs, a jumping knowledge module is integrated, enabling information sharing across different layers and providing the model with the flexibility to select the appropriate representation depth based on the specific situation. By enhancing the quality of node representations, SSP-AA further boosts the performance of GraphSAGE in various prediction tasks involving graph-structured data.

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Section: Link Prediction Methodsmentioning

confidence: 99%

Sparse Subgraph Prediction Based on Adaptive Attention

Gao

et al. 2023

Applied Sciences

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“…We compare HL-Louvain with four baseline methods. There are two traditional community discovery algorithms, Louvain [19] and LPA [46], as well as two state-of-the-art algorithms, UBP [47] and HLDA [48].…”

Section: Baseline Approachesmentioning

confidence: 99%

An Efficient Community Detection Algorithm in Online Social Networks

Han

Shi

Liu

et al. 2023

Preprint

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The advent of social networks has brought about a paradigm shift in the conventional modes of social interaction and information exchange. Consequently, there is an increasing scholarly interest in the precise detection of communities and analysis of network structures. However, most of the prevailing methods for community detection suffer from limitations in accuracy and efficiency due to the requirement of manually configuring attribute vector dimensions during the extraction of attribute information. Moreover, these algorithms often neglect the pervasive influence of users within the global network and their capacity to disseminate information, thereby undermining the accuracy of community detection. To address these challenges, this study proposes a novel community detection algorithm, named HL Louvain, which draws upon the Hypertext Induced Topic Search (HITS) technique. The HL Louvain algorithm initially applies graph compression to the entire network and subsequently leverages the HITS algorithm to extract global node characteristics. By combining semantic attribute information with local user features, the algorithm defines the collective influence of users. This, in turn, facilitates accurate community detection by modifying the iterative approach of the conventional Louvain algorithm. Furthermore, the algorithm significantly enhances accuracy and stability by autonomously optimizing the iterative process to determine the dimensionality of the attribute vector and the number of topics within the identified network. Experimental evaluations conducted on three distinct Twitter datasets with varying degrees of complexity, as well as a public dataset, demonstrate that the HL Louvain algorithm outperforms other state-of-the-art algorithms in terms of accuracy and stability.

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“…Second, he identified network structure properties and user interest preferences as important factors affecting the link prediction process in online social networks. By designing a friend recommendation model, he incorporates the user's relationship information and interest preference features into the community detection algorithm, further improving the prediction process [7].…”

Section: Related Workmentioning

confidence: 99%

Design of Accurate Placement Method for Film and Television Advertisements Based on Digital Twin and Data Mining

2023

mediacr

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With the development of economy, the Internet has developed rapidly as a new economic platform. At present, the Internet has become the best carrier of advertising and has huge commercial value. However, the extensive placement of advertisements not only brings troubles to Internet users, but also wastes the cost of advertisers. It cannot achieve the corresponding publicity effect, and cannot stimulate the consumer psychology of users, so the rate of return is low. This paper takes the network platform as the environment and data mining technology and digital twins as the technical support to study the dynamic clustering analysis advertising delivery algorithm and the collaborative filtering advertising delivery algorithm. This paper conducts experiments with KDD CUP 2012-Track 2 as the main dataset. The results show that the dynamic clustering analysis based on the K-means algorithm can greatly reduce the classification prediction error of users and film and television advertisements, and can better improve the accuracy of film and television advertisements. The error rate greater than 0.5 accounted for only 11.4% of the total number of advertisements. In the collaborative filtering algorithm, the performance of WSO is significantly better than that of the Slop One algorithm, and the error value becomes smaller when the number of test days is 15. At this time, the average MAE value of WSO is 0.916.

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Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks

Cited by 8 publications

References 31 publications

Sparse Subgraph Prediction Based on Adaptive Attention

Sparse Subgraph Prediction Based on Adaptive Attention

An Efficient Community Detection Algorithm in Online Social Networks

Design of Accurate Placement Method for Film and Television Advertisements Based on Digital Twin and Data Mining

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