An Ensemble Approach to Link Prediction

Liang, Dongfang; Ma, Shuai; Aggarwal, Charų C.; Ma, Tiejun; Huai, Jinpeng

doi:10.1109/tkde.2017.2730207

Cited by 55 publications

(20 citation statements)

References 42 publications

(84 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The intuition derived by latent factor models for link prediction (Hoff 2008;Menon and Elkan 2011;Duan et al 2017) and collaborative filtering (Koren and Bell 2015) helps us recognize R = [r 1 . .…”

Section: Approach Motivation and Overviewmentioning

confidence: 99%

Unsupervised evaluation of multiple node ranks by reconstructing local structures

2020

View full text Add to dashboard Cite

A problem that frequently occurs when mining complex networks is selecting algorithms with which to rank the relevance of nodes to metadata groups characterized by a small number of examples. The best algorithms are often found through experiments on labeled networks or unsupervised structural community quality measures. However, new networks could exhibit characteristics different from the labeled ones, whereas structural community quality measures favor dense congregations of nodes but not metadata groups spanning a wide breadth of the network. To avoid these shortcomings, in this work we propose using unsupervised measures that assess node rank quality across multiple metadata groups through their ability to reconstruct the local structures of network nodes; these are retrieved from the network and not assumed. Three types of local structures are explored: linked nodes, nodes up to two hops away and nodes forming triangles. We compare the resulting measures alongside unsupervised structural community quality ones to the AUC and NDCG of supervised evaluation in one synthetic and four real-world labelled networks. Our experiments suggest that our proposed local structure measures are often more accurate for unsupervised pairwise comparison of ranking algorithms, especially when few example nodes are provided. Furthermore, the ability to reconstruct the extended neighborhood, which we call HopAUC, manages to select a near-best among many ranking algorithms in most networks.

show abstract

Section: Approach Motivation and Overviewmentioning

confidence: 99%

Unsupervised evaluation of multiple node ranks by reconstructing local structures

2020

View full text Add to dashboard Cite

show abstract

“…b*The number of distinct partitions of N elements into groups is , which grows faster than any finite power of N [16]. c* d express the dimensionality of the embedding.…”

Section: F Computational Complexitymentioning

confidence: 99%

“…Graph representation-based methods pay more attention to the characteristics of the network. Most of the traditional machine learning methods try to classify the non-existence edges according to the labels of the observed edges by extracting the structural features of the network [15,16]. In order to improve the computational efficiency of the graph algorithm, some scholars have proposed the graph embedding techniques that represent a graph as a low dimensional vector, and they are successfully applied in the link prediction problem [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Multi-Resolution Prediction Model Based on Community Relevance for Missing Links Prediction

et al. 2020

View full text Add to dashboard Cite

The existing research demonstrates that the link prediction algorithm which based on community similarity has better prediction performance than that of other node similarity-based methods, and it is more suitable for predicting the probability of the missing links between node-pairs with far distance. However, the disadvantage of these community similarity-based methods is the resolution of prediction accuracy is very low, which resulting in the existence probability of the missing links between node-pairs within a community or between a specific pair of communities is the same. In addition, the link prediction algorithms which based on multi-resolution community division can calculate the existence probability of missing links under different resolutions, but the relevance between communities had not taken into account, which makes it difficult to predict the existence probability of target links if the number of interconnections between communities is small. Combining the advantages of these two algorithms, we propose a more realistic link prediction model which based on a novel quasi-local community relevance index under multi-resolution community division. The performance of our algorithms is demonstrated by comparing with other well-known methods on two kinds of networks in different scales. The experiment results indicate that our approaches are very competitive.

show abstract

“…. , n. Motivated by latent factor models for link prediction [23] and collaborative filtering [24], we consider R = [r 1 . .…”

Section: Link Ranksmentioning

confidence: 99%

LinkAUC: Unsupervised Evaluation of Multiple Network Node Ranks Using Link Prediction

Krasanakis

Papadopoulos

Kompatsiaris

2019

Complex Networks and Their Applications VIII

View full text Add to dashboard Cite

An emerging problem in network analysis is ranking network nodes based on their relevance to metadata groups that share attributes of interest, for example in the context of recommender systems or node discovery services. For this task, it is important to evaluate ranking algorithms and parameters and select the ones most suited to each network. Unfortunately, large real-world networks often comprise sparsely labelled nodes that hinder supervised evaluation, whereas unsupervised measures of community quality, such as density and conductance, favor structural characteristics that may not be indicative of metadata group quality. In this work, we introduce LinkAUC, a new unsupervised approach that evaluates network node ranks of multiple metadata groups by measuring how well they predict network edges. We explain that this accounts for relation knowledge encapsulated in known members of metadata groups and show that it enriches density-based evaluation. Experiments on one synthetic and two real-world networks indicate that LinkAUC agrees with AUC and NDCG for comparing ranking algorithms more than other unsupervised measures.

show abstract

An Ensemble Approach to Link Prediction

Cited by 55 publications

References 42 publications

Unsupervised evaluation of multiple node ranks by reconstructing local structures

Unsupervised evaluation of multiple node ranks by reconstructing local structures

Multi-Resolution Prediction Model Based on Community Relevance for Missing Links Prediction

LinkAUC: Unsupervised Evaluation of Multiple Network Node Ranks Using Link Prediction

Contact Info

Product

Resources

About