Link prediction

Li, Ronghua; Yu, Jeffrey Xu; Liu, Jianquan

doi:10.1145/2063576.2063741

Cited by 56 publications

(9 citation statements)

References 24 publications

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“…In this measure, the probability of establishing links for a node pair is high, if they are having more number of similar neighbors. It can be defined in Equation () 11

L P_{SR} (x, y) = γ \frac{\sum_{a \in N (x)} \sum_{b \in N (y) L P_{SR (a, b)}}}{| N (x) | | N (y) |},

where LP SR ( x , y ) is the similarity score between x and y using SimRank.…”

Section: Methodologies and Background Detailsmentioning

confidence: 99%

“…We have extracted the features for nonexisting links from the topological structure of the network by computing various local and global similarity measures. The actors who are having high similarity scores are more likely to be predicted. Instead of drawing a conclusion from any individual similarity measures, we have proposed machine learning models that combine all the structural features for link prediction. The different types of similarity measures that we have considered in this article are common neighbor (CN), 7 Jaccard coefficient (JC), 8 preferential attachment (PA), 9 Adamic/Adar index (AI), 10 Karz measure, 3 SimRank, 11 and so on.…”

Section: Introductionmentioning

confidence: 99%

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Supervised link prediction using structured‐based feature extraction in social network

Kumari

Behera

Sahoo

et al. 2020

Concurrency and Computation

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Summary Social network analysis (SNA) has attracted a lot of attention in several domains in the past decades. It can be of 2‐folds: one is content‐based, and another one is structured‐based analysis. Link prediction is one of the emerging research problems, which comes under structured‐based analysis that deals with predicting the missing link, which is likely to appear in the future. In this article, the supervised machine learning techniques have been implemented to predict the possibilities of establishing the links in future. The major contribution in this article lies in feature construction from the topological structure of the network. Several structured‐based similarity measures have been considered for preparing the feature vector for each nonexisting links in the network. The performance of the proposed algorithm has been extensively validated by comparing with other link prediction algorithms using both real‐world and synthetic data sets.

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“…In this measure, the probability of establishing links for a node pair is high, if they are having more number of similar neighbors. It can be defined in Equation () 11

L P_{SR} (x, y) = γ \frac{\sum_{a \in N (x)} \sum_{b \in N (y) L P_{SR (a, b)}}}{| N (x) | | N (y) |},

where LP SR ( x , y ) is the similarity score between x and y using SimRank.…”

Section: Methodologies and Background Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supervised link prediction using structured‐based feature extraction in social network

Kumari

Behera

Sahoo

et al. 2020

Concurrency and Computation

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“…e maximum likelihood approach predicted links by utilizing specific parameters, and the probabilistic method could employ the trained model to forecast links [15]. However, these two approaches did not apply to the large-scale networks.…”

Section: Link Prediction Link Predictionmentioning

confidence: 99%

Keywords-Driven Paper Recommendation Based on Mobile Edge Computing Environment Framework

Liu

Wang

Ren

et al. 2022

Scientific Programming

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In the cloud computing era, a paper recommender system is usually deployed on the cloud server and return recommendation results to readers directly. However, considering the paper recommender system, processing tremendous paper citation data on the cloud cannot provide fine-grained personalized and real-time recommendations for each reader because these recommended papers from the cloud are far from readers and probably not correlated strongly with each other for helping each reader research further and deeper in the interested field. Recently, the edge-cloud collaboration-based recommender system has been used for releasing parts of the cloud computing task to the edge and provides the recommendation near the client. Based on the edge computing recommender system, a keywords-driven and weight-aware paper recommendation approach is presented, namely, LP-PRk+ w (link prediction-paper recommendation), to enable intelligent, personalized, and efficient paper recommendation services in the mobile edge computing environment. Specifically, the whole paper recommendation process mainly covers two parts: optimizing the existing paper citation graph via introducing a weighted similarity (i.e., building a weighted paper correlation graph) and then recommending a set of correlated papers according to the weighted paper correlation graph and the users’ query keywords. Experiments on a real-world paper correlation dataset, Hep-Th, show the capability of our proposal for improving the paper recommendation performance and its superiority against other related solutions.

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“…It is known that the basic idea of using random walk or random walk based kernels [17][18][19][20] for PPI prediction is that good interacting candidates usually are not faraway from the start node, e.g., only 2,3 edges away in the network. Consequently, for some existing network-level link prediction methods, testing nodes have been chosen to be within a certain distance range, which largely contributes to their good performance reported.…”

Section: Detection Of Interacting Pairs Far Apart In the Networkmentioning

confidence: 99%

“…Topological features, such as the number of neighbors, can be collected for nodes and then are used to measure the similarity for any given node pair to make PPI prediction for the corresponding proteins [12][13][14][15]. Inspired by the PageRank algorithm [16], variants of random walk-based methods have been proposed to go beyond these node centric topological features to get the whole network involved; the probability of interaction between given two proteins is measured in terms of how likely a random walk in the network starting at one node will reach the other node [17][18][19]. These methods are suitable for PPI prediction in cases when the task is to find all interacting partners for a particular protein, by using it as the start node for random walks.…”

Section: Introductionmentioning

confidence: 99%

Inference of protein-protein interaction networks from multiple heterogeneous data

Huang

Liao

2015

Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

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Protein-protein interaction (PPI) prediction is a central task in achieving a better understanding of cellular and intracellular processes. Because high-throughput experimental methods are both expensive and time-consuming, and are also known of suffering from the problems of incompleteness and noise, many computational methods have been developed, with varied degrees of success. However, the inference of PPI network from multiple heterogeneous data sources remains a great challenge. In this work, we developed a novel method based on approximate Bayesian computation and modified differential evolution sampling (ABC-DEP) and regularized laplacian (RL) kernel. The method enables inference of PPI networks from topological properties and multiple heterogeneous features including gene expression and Pfam domain profiles, in forms of weighted kernels. The optimal weights are obtained by ABC-DEP, and the kernel fusion built based on optimal weights serves as input to RL to infer missing or new edges in the PPI network. Detailed comparisons with control methods have been made, and the results show that the accuracy of PPI prediction measured by AUC is increased by up to 23 %, as compared to a baseline without using optimal weights. The method can provide insights into the relations between PPIs and various feature kernels and demonstrates strong capability of predicting faraway interactions that cannot be well detected by traditional RL method.

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Link prediction

Cited by 56 publications

References 24 publications

Supervised link prediction using structured‐based feature extraction in social network

Supervised link prediction using structured‐based feature extraction in social network

Keywords-Driven Paper Recommendation Based on Mobile Edge Computing Environment Framework

Inference of protein-protein interaction networks from multiple heterogeneous data

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