Evaluating Link Prediction Accuracy in Dynamic Networks with Added and Removed Edges

Junuthula, Ruthwik R.; Xu, Kevin S.; Devabhaktuni, Vijay

doi:10.1109/bdcloud-socialcom-sustaincom.2016.63

Cited by 30 publications

(21 citation statements)

References 15 publications

(80 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [4], authors discuss on the problem of current link prediction approaches. We have pointed out that evaluating link predictions based on deleted edges is not suitable considering the dynamics of social networks.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluations of Similarity Measures on VK for Link Prediction

Lee

Tukhvatov

2018

Data Sci. Eng.

View full text Add to dashboard Cite

Recommender system is one of the most important components for many companies and social networks such as Facebook and YouTube. A recommendation system consists of algorithms which allow to predict and recommend friends or products. This paper studies to facilitate finding like-minded people with same interests in social networks. In our research, we used real data from the most popular social network in Russia, VK (Vkontakte). The study is motivated on the assumption that similarity breeds connection. We evaluate well-known similarity measures in the field on our collected VK datasets and find limited performance results. The result shows that majority of users in VK tend not to add possible users with whom they have common acquaintances. We also propose a topology-based similarity measure to predict future friends. Then, we compare our results with the results of other well-known methods and discuss differences.

show abstract

Section: Discussionmentioning

confidence: 99%

“…As of January 2017, daily average audience is about 87 million visitors, 3 with more than 410 million registered users. 4 Any user in VK has a profile which contains various information. First and last names are mandatory fields, and other data such as birthday, city and interests are optional.…”

Section: Vkontaktementioning

confidence: 99%

Evaluations of Similarity Measures on VK for Link Prediction

Lee

Tukhvatov

2018

Data Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…However, the removed links in the near future, as a significant aspect of DNLP, are not characterized by PR curve and thus PRAUC may lose its effectiveness in this case. Junuthula et al [44] restricted the measurements to only part of node pairs and proposed the Geometric Mean of AUC and PRAUC (GMAUC) for the added and removed links, which can better reflect the dynamic performance. Li et al [29] use SumD that counts the differences between the predicted network and the true one, evaluating link prediction methods in a more strict way.…”

Section: Evaluation Metricsmentioning

confidence: 99%

E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction

Chen

Zhang

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

128

View full text Add to dashboard Cite

Predicting the potential relations between nodes in networks, known as link prediction, has long been a challenge in network science. However, most studies just focused on link prediction of static network, while real-world networks always evolve over time with the occurrence and vanishing of nodes and links. Dynamic network link prediction thus has been attracting more and more attention since it can better capture the evolution nature of networks, but still most algorithms fail to achieve satisfied prediction accuracy. Motivated by the excellent performance of Long Short-Term Memory (LSTM) in processing time series, in this paper, we propose a novel Encoder-LSTM-Decoder (E-LSTM-D) deep learning model to predict dynamic links end to end. It could handle long term prediction problems, and suits the networks of different scales with finetuned structure. To the best of our knowledge, it is the first time that LSTM, together with an encoder-decoder architecture, is applied to link prediction in dynamic networks. This new model is able to automatically learn structural and temporal features in a unified framework, which can predict the links that never appear in the network before. The extensive experiments show that our E-LSTM-D model significantly outperforms newly proposed dynamic network link prediction methods and obtain the state-of-the-art results.

show abstract

“…Evaluation measures used in the area of link prediction are embraced from other research areas i.e., classification, information retrieval [39]. These evaluation measures can be classified into two categorize: (1) threshold curves and (2) fixed threshold [58][101] [24].…”

Section: Performance Evaluation Measuresmentioning

confidence: 99%

“…It only considers the positive links for instead of negative links. Since, in periodic link prediction, it is required to predict removed links for that PR curve is not suitable [39].…”

Section: Pr: Pr Is Abbreviation Of Precision-recall Curve Itmentioning

confidence: 99%

A Comprehensive Survey of Link Prediction Techniques for Social Network

Samad¹,

Qadir²,

Nawaz³

et al. 2020

EAI Endorsed Transactions on Industrial Networks and Intelligen

View full text Add to dashboard Cite

A growing trend of using social networking sites is attracting researchers to study and analyze different aspects of social network. Besides many problems, link prediction is a fascinating problem in the field of social network analysis (SNA). Link prediction, in social network analysis, is a task of identifying the missing links and predicting the new links. Several researchers have proposed solutions for the link prediction problem during the past two decades. However, there is a need to provide comprehensive overview of the significant contributions for a thorough analysis. The objective of this review is to summaries and discuss the existing link prediction algorithms in a common context for an unbiased analysis. The extensive review is presented by constructing the systematical category for proposed algorithms, selected problems, evaluation measures along with selected network datasets. Finally, applications of link prediction are discussed.

show abstract

Evaluating Link Prediction Accuracy in Dynamic Networks with Added and Removed Edges

Cited by 30 publications

References 15 publications

Evaluations of Similarity Measures on VK for Link Prediction

Evaluations of Similarity Measures on VK for Link Prediction

E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction

A Comprehensive Survey of Link Prediction Techniques for Social Network

Contact Info

Product

Resources

About