Using Friendship Ties and Family Circles for Link Prediction

2010 ◽  
pp. 97-113 ◽  
Author(s):  
Elena Zheleva ◽  
Lise Getoor ◽  
Jennifer Golbeck ◽  
Ugur Kuter
Keyword(s):  
Link Prediction

Analysis of Link Prediction Method for Link Recovery in Mobile Ad-hoc Network

2017 ◽  
Vol 7 (5) ◽  
pp. 849-854
Author(s):  
◽  
Jatinder Kaur ◽  
Danvir Mandal ◽  
Rajneesh Talwar ◽  
◽  
...  
Keyword(s):  
Ad Hoc Network ◽  
Link Prediction ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Prediction Method ◽  
Mobile Ad Hoc

A Mobile User Preference Prediction Method Based on Trust and Link Prediction

2014 ◽  
Vol 35 (12) ◽  
pp. 2972-2977
Author(s):  
Hua Geng ◽  
Xiang-wu Meng ◽  
Yan-cui Shi
Keyword(s):  
Link Prediction ◽  
Prediction Method ◽  
Mobile User ◽  
User Preference

Enhancing link prediction via network reconstruction

2019 ◽  
Vol 534 ◽  
pp. 122346 ◽  
Author(s):  
Mei Wu ◽  
Shunyao Wu ◽  
Qi Zhang ◽  
Chuanyu Xue ◽  
Hongsheng Kan ◽  
...  
Keyword(s):  
Link Prediction ◽  
Network Reconstruction

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

Link Prediction in Dynamic Networks Based on Machine Learning

2020 ◽  
Author(s):  
Jiachen Liu ◽  
Yinan Jiang ◽  
Yashen Wang ◽  
Haiyong Xie ◽  
Jie Ni
Keyword(s):  
Machine Learning ◽  
Link Prediction ◽  
Dynamic Networks

scholarly journals ALPINE: Active Link Prediction Using Network Embedding

2021 ◽  
Vol 11 (11) ◽  
pp. 5043
Author(s):  
Xi Chen ◽  
Bo Kang ◽  
Jefrey Lijffijt ◽  
Tijl De Bie
Keyword(s):  
Active Learning ◽  
Protein Interactions ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Real Data ◽  
Network Embedding ◽  
Protein Protein Interactions ◽  
Additional Information ◽  
The Cost ◽  
Active Link

Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, the prediction of protein–protein interactions, and the identification of hidden relationships in a crime network. Several link prediction algorithms, notably those recently introduced using network embedding, are capable of doing this by just relying on the observed part of the network. Often, whether two nodes are linked can be queried, albeit at a substantial cost (e.g., by questionnaires, wet lab experiments, or undercover work). Such additional information can improve the link prediction accuracy, but owing to the cost, the queries must be made with due consideration. Thus, we argue that an active learning approach is of great potential interest and developed ALPINE (Active Link Prediction usIng Network Embedding), a framework that identifies the most useful link status by estimating the improvement in link prediction accuracy to be gained by querying it. We proposed several query strategies for use in combination with ALPINE, inspired by the optimal experimental design and active learning literature. Experimental results on real data not only showed that ALPINE was scalable and boosted link prediction accuracy with far fewer queries, but also shed light on the relative merits of the strategies, providing actionable guidance for practitioners.

Towards effective link prediction: A hybrid similarity model

2020 ◽  
pp. 1-14
Author(s):  
Longjie Li ◽  
Lu Wang ◽  
Hongsheng Luo ◽  
Xiaoyun Chen
Keyword(s):  
Link Prediction ◽  
Structural Similarity ◽  
Research Direction ◽  
Structural Features ◽  
Proposed Model ◽  
Similarity Model ◽  
Weight Calculation ◽  
Stable Performance ◽  
Grey Relation ◽  
Important Research Direction

Link prediction is an important research direction in complex network analysis and has drawn increasing attention from researchers in various fields. So far, a plethora of structural similarity-based methods have been proposed to solve the link prediction problem. To achieve stable performance on different networks, this paper proposes a hybrid similarity model to conduct link prediction. In the proposed model, the Grey Relation Analysis (GRA) approach is employed to integrate four carefully selected similarity indexes, which are designed according to different structural features. In addition, to adaptively estimate the weight for each index based on the observed network structures, a new weight calculation method is presented by considering the distribution of similarity scores. Due to taking separate similarity indexes into account, the proposed method is applicable to multiple different types of network. Experimental results show that the proposed method outperforms other prediction methods in terms of accuracy and stableness on 10 benchmark networks.

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