Link prediction using node information on local paths

Aziz, Furqan; Gul, Haji; Ishtiaq, Muhammad; Uddin, Imad

doi:10.1016/j.physa.2020.124980

Cited by 43 publications

(14 citation statements)

References 33 publications

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“… Moradabadi and Meybodi (2018) proposed a strategy of learning automata for link prediction in weighted social networks. Aziz et al (2020) proposed a novel link prediction method that aims at improving the accuracy of existing path-based methods by incorporating information about the nodes along local paths. Tang R. et al (2021) proposed a framework based on multiple types of consistency between embedding vectors (MulCEVs).…”

Section: Related Workmentioning

confidence: 99%

Social Relationship Prediction Integrating Personality Traits and Asymmetric Interactions

Bao

et al. 2022

Front. Psychol.

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Social networks have become an important way for users to find friends and expand their social circle. Social networks can improve users’ experience by recommending more suitable friends to them. The key lies in improving the accuracy of link prediction, which is also the main research issue of this study. In the study of personality traits, some scholars have proved that personality can be used to predict users’ behavior in social networks. Based on these studies, this study aims to improve the accuracy of link prediction in directed social networks. Considering the integration of personality link preference and asymmetric interaction into the link prediction model of social networks, a four-dimensional link prediction model is proposed. Through comparative experiments, it is proved that the four-dimensional social relationship prediction model proposed in this study is more accurate than the model only based on similarity. At the same time, it is also verified that the matching degree of personality link preference and asymmetric interaction intensity in the model can help improve the accuracy of link prediction.

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Section: Related Workmentioning

confidence: 99%

Social Relationship Prediction Integrating Personality Traits and Asymmetric Interactions

Bao

et al. 2022

Front. Psychol.

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“…Recently, with the rapid development of network embedding technology, various embedding models have emerged. These methods learn the low-dimensional representation of nodes in large-scale networks, which performs well in link prediction (Aziz et al, 2020;Cen et al, 2019), node classification (Dong, Chawla, & Swami, 2017), and community detection (Chen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Academic Collaborator Recommendation Based on Attributed Network Embedding

2022

Journal of Data and Information Science

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Purpose Based on real-world academic data, this study aims to use network embedding technology to mining academic relationships, and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks. Design/methodology/approach We propose an academic collaborator recommendation model based on attributed network embedding (ACR-ANE), which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes. The non-local neighbors for scholars are defined to capture strong relationships among scholars. A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space. Findings 1. The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors. 2. It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously. Research limitations The designed method works for static networks, without taking account of the network dynamics. Practical implications The designed model is embedded in academic collaboration network structure and scholarly attributes, which can be used to help scholars recommend potential collaborators. Originality/value Experiments on two real-world scholarly datasets, Aminer and APS, show that our proposed method performs better than other baselines.

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“…Local similarity methods explore local information of the disconnected nodes such as common neighbors and node degree, and include Common Neighbors (CNs) [ 8 ], Jaccard coefficient (JC) [ 9 ], Preferential Attachment (PA) [ 10 ], the Adamic–Adar (AA) index [ 11 ], Resource Allocation (RA) [ 12 ], Cosine similarity, and the Salton index (SI) [ 13 ]. These methods achieve good results in many cases due to lower computational complexity and simple implementation [ 14 ]. Global similarity methods utilize the topological information of the whole network for link prediction.…”

Section: Introductionmentioning

confidence: 99%

Link Prediction in Complex Networks Using Recursive Feature Elimination and Stacking Ensemble Learning

Wang

Jiao

Wang

2022

Entropy

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Link prediction is an important task in the field of network analysis and modeling, and predicts missing links in current networks and new links in future networks. In order to improve the performance of link prediction, we integrate global, local, and quasi-local topological information of networks. Here, a novel stacking ensemble framework is proposed for link prediction in this paper. Our approach employs random forest-based recursive feature elimination to select relevant structural features associated with networks and constructs a two-level stacking ensemble model involving various machine learning methods for link prediction. The lower level is composed of three base classifiers, i.e., logistic regression, gradient boosting decision tree, and XGBoost, and their outputs are then integrated with an XGBoost model in the upper level. Extensive experiments were conducted on six networks. Comparison results show that the proposed method can obtain better prediction results and applicability robustness.

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Link prediction using node information on local paths

Cited by 43 publications

References 33 publications

Social Relationship Prediction Integrating Personality Traits and Asymmetric Interactions

Social Relationship Prediction Integrating Personality Traits and Asymmetric Interactions

Academic Collaborator Recommendation Based on Attributed Network Embedding

Link Prediction in Complex Networks Using Recursive Feature Elimination and Stacking Ensemble Learning

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