Efficient incremental dynamic link prediction algorithms in social network

Zhang, Zhongbao; Wen, Jian; Sun, Lijun; Deng, Qiaoyu; Su, Sen; Yao, Pengyan

doi:10.1016/j.knosys.2017.06.035

Cited by 38 publications

(11 citation statements)

References 30 publications

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“…Nonetheless, because of the huge computational time of Probabilistic graph models, their application is not practicable in real-life applications. 54 The problem of link prediction is typically formulated as a binary classification task (supervised learning) or a sorting problem on the node pairs (unsupervised learning). Some studies in this area have shown that the latter is not able to deal with the dynamics, interdependencies, and other features in the networks.…”

Section: Related Workmentioning

confidence: 99%

An efficient algorithm for link prediction based on local information: Considering the effect of node degree

Mumin

Shi

Liu

2021

Concurrency and Computation

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There has been much interest in link prediction research with significant studies on how to predict missing links or future links in a network based on observed information. However, the key solution to tackle the link prediction problem is how to measure the similarity between the nodes in a network with higher accuracy. Several techniques have been proposed that utilize the similarity between nodes to estimate their proximity in the network. In this paper, an efficient link prediction algorithm that predicts relationships between links using the network structure is proposed. This algorithm uses common neighbors in addition to the degree distribution of the nodes to estimate the possibility of the presence of a link between two nodes in a network based on local information. Extensive experiments are carried out and compared with 12 standard similarity-based methods using seven real-world datasets. The experimental results show that our method has higher prediction accuracy compared with most of the local information based methods like the Common Neighbor and Preferential Attachment.It is also competitive with the quasi-local indicators such as LP and global indicators like Katz, with a lower computational complexity than the two.

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Section: Related Workmentioning

confidence: 99%

An efficient algorithm for link prediction based on local information: Considering the effect of node degree

Mumin

Shi

Liu

2021

Concurrency and Computation

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“…Traditional link prediction methods often select test dataset and training dataset randomly, and repeat experiments to evaluate the effect of link prediction. However, the real world is mostly a dynamic network, and the edges and nodes are constantly changing over time [43]. Therefore, the proposed DGATNE model takes snapshots of the dynamic network at equal intervals in time series.…”

Section: The Evaluation Index Of Link Prediction Modelmentioning

confidence: 99%

Research on the Link Prediction Model of Dynamic Multiplex Social Network Based on Improved Graph Representation Learning

Xia

Yin

2021

IEEE Access

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“…Z. Zhang et al [108] proposed the two efficient and dynamic increment algorithms based on the improved latent space and resource allocation to predict links dynamically according to the SN structure updates referred to dynamic link prediction algorithms based on improved latent space (DLP-ILS) and dynamic link prediction algorithms based on improved resource allocation (DLP-IRA). The advantage of DLP-IRA and DLP-ILS is that they only need to recalculate the graph when being updated partially.…”

Section: ) Latent-feature-based Modelsmentioning

confidence: 99%

“…The advantage of DLP-IRA and DLP-ILS is that they only need to recalculate the graph when being updated partially. Conversely, the disadvantage is associated with the node adjacency relationship that does not have the common neighbors processed serially, not parallel [108]. Furthermore, Y. Li et al [109] proposed the utility-based link prediction method based on considering that individual preferences are the main reason behind the decision to form links.…”

Section: ) Latent-feature-based Modelsmentioning

confidence: 99%

Taxonomy of Link Prediction for Social Network Analysis: A Review

2020

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Link prediction is a technique to forecast future new or missing relationships between entities based on the current network information. Graph theory and network science are theoretical concepts that have influenced the link prediction research. Although previous reviews clearly outlined the link prediction research, it was focused on describing prediction approaches only. However, analysis of related studies identified other components that influence link prediction. This review aims to present a continued review and introduce the taxonomy of link prediction using three main components: the prediction approaches, prediction features, and prediction measurements. Each component has been detailed using its own taxonomy available at the present review. Furthermore, this review compares the prediction approaches and prediction features also benchmark algorithms and measurement methods of previous link prediction studies. In conclusion, the previous studies mostly focused on structural features and similarity-based approaches, while measuring the proposed methods using the Area Under the Curve (AUC) score. The proposed link prediction taxonomy can guide the researchers to generate new ideas and innovations that contribute to the link prediction research.

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Efficient incremental dynamic link prediction algorithms in social network

Cited by 38 publications

References 30 publications

An efficient algorithm for link prediction based on local information: Considering the effect of node degree

An efficient algorithm for link prediction based on local information: Considering the effect of node degree

Research on the Link Prediction Model of Dynamic Multiplex Social Network Based on Improved Graph Representation Learning

Taxonomy of Link Prediction for Social Network Analysis: A Review

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