scholarly journals Link prediction based on combined influence and effective path

2019 ◽  
Vol 33 (22) ◽  
pp. 1950249 ◽  
Author(s):  
Yang Tian ◽  
Han Li ◽  
Xuzhen Zhu ◽  
Hui Tian
Keyword(s):  
Link Prediction ◽  
Low Complexity ◽  
High Accuracy ◽  
Prediction Performance ◽  
Topological Similarity ◽  
Large Influence ◽  
Benchmark Datasets ◽  
Local Path ◽  
Transmission Capability ◽  
Effective Path

Link prediction based on topological similarity in complex networks obtains more and more attention both in academia and industry. Most researchers believe that two unconnected endpoints can possibly make a link when they have large influence, respectively. Through profound investigations, we find that at least one endpoint possessing large influence can easily attract other endpoints. The combined influence of two unconnected endpoints affects their mutual attractions. We consider that the greater the combined influence of endpoints is, the more the possibility of them producing a link. Therefore, we explore the contribution of combined influence for similarity-based link prediction. Furthermore, we find that the transmission capability of path determines the communication possibility between endpoints. Meanwhile, compared to the local and global path, the quasi-local path balances high accuracy and low complexity more effectually in link prediction. Therefore, we focus on the transmission capabilities of quasi-local paths between two unconnected endpoints, which is called effective paths. In this paper, we propose a link prediction index based on combined influence and effective path (CIEP). A large number of experiments on 12 real benchmark datasets show that in most cases CIEP is capable of improving the prediction performance.

scholarly journals Link prediction based on the powerful combination of endpoints and neighbors

2020 ◽  
Vol 34 (28) ◽  
pp. 2050269
Author(s):  
Tianrun Gao ◽  
Xuzhen Zhu
Keyword(s):  
Complex Networks ◽  
Performance Improvement ◽  
Link Prediction ◽  
Prediction Models ◽  
Important Research ◽  
Topological Similarity ◽  
Real Network ◽  
Model Experiments ◽  
Large Influence ◽  
Proposed Model

Performance improvement of topological similarity-based link prediction models becomes an important research in complex networks. In the models based on node influence, researchers mainly consider the roles of endpoints or neighbors. Through investigations, we find that an endpoint with large influence has many neighbors. Meanwhile, the neighbors connect with more nodes besides endpoint, meaning that the endpoint can transmit extensive influence by the powerful combination of itself and neighbors. In addition, we evaluate the node influence by degree because the degree represents the number of neighbors accurately. In this paper, through focusing on the degree of endpoints and neighbors, we propose the powerful combination of endpoints and neighbors (PCEN) model. Experiments on twelve real network datasets demonstrate that the proposed model has better prediction performances than the traditional models.

Roles of degree, H-index and coreness in link prediction of complex networks

2018 ◽  
Vol 32 (16) ◽  
pp. 1850197 ◽  
Author(s):  
Xuzhen Zhu ◽  
Yujie Yang ◽  
Lanxi Li ◽  
Shimin Cai
Keyword(s):  
Complex Networks ◽  
Link Prediction ◽  
H Index ◽  
Topological Similarity ◽  
Benchmark Datasets ◽  
First Time

Link prediction based on topological similarity attracts more and more interests. Traditionally, researchers almost focus on utility of the paths between two unlinked endpoints, but pay little attention to the influence of the endpoints with only degree considered. Through profound investigations, we find, besides of degree, H-index and coreness also can play important roles in link prediction as the influence of endpoint especially in models based on representative SRW which is for the first time introduce influence into link prediction. In this paper, we mainly research degree, H-index and coreness in SRW-based models to explore their roles in accurate link prediction. Extensive experiments on twelve real benchmark datasets suggest that in most cases H-index serves as a better tradeoff in accurate link prediction than either degree or coreness.

Robust MVDR Estimation via Polynomial Rooting for MC-CDMA Carrier Frequency Offset

2013 ◽  
Vol 284-287 ◽  
pp. 2687-2693
Author(s):  
Ing Jiunn Su ◽  
Chiao Chan Huang
Keyword(s):  
Carrier Frequency ◽  
Code Division Multiple Access ◽  
Multiple Access ◽  
Carrier Frequency Offset ◽  
Low Complexity ◽  
Frequency Offset ◽  
High Accuracy ◽  
Cdma Systems ◽  
Code Division Multiple ◽  
Polynomial Rooting

In this letter, we present a blind carrier frequency offset (CFO) estimator by exploiting the polynomial rooting technique for multicarrier-code division multiple access (MC-CDMA) systems. Relative high accuracy and low-complexity to the CFO estimation can be achieved by rooting a polynomial. Simulation results are provided for illustrating the effectiveness of the proposed blind polynomial rooting estimator.

scholarly journals Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

2020 ◽  
Vol 34 (04) ◽  
pp. 4844-4851
Author(s):  
Fanghui Liu ◽  
Xiaolin Huang ◽  
Yudong Chen ◽  
Jie Yang ◽  
Johan Suykens
Keyword(s):  
State Of The Art ◽  
Sampling Strategy ◽  
Prediction Performance ◽  
Current State ◽  
Kernel Approximation ◽  
Sampling Process ◽  
New Strategy ◽  
The Matrix ◽  
Benchmark Datasets ◽  
Random Fourier Features

In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation. Compared to the current state-of-the-art method that uses the leverage weighted scheme (Li et al. 2019), our new strategy is simpler and more effective. It uses kernel alignment to guide the sampling process and it can avoid the matrix inversion operator when we compute the leverage function. Given n observations and s random features, our strategy can reduce the time complexity for sampling from O(ns2+s3) to O(ns2), while achieving comparable (or even slightly better) prediction performance when applied to kernel ridge regression (KRR). In addition, we provide theoretical guarantees on the generalization performance of our approach, and in particular characterize the number of random features required to achieve statistical guarantees in KRR. Experiments on several benchmark datasets demonstrate that our algorithm achieves comparable prediction performance and takes less time cost when compared to (Li et al. 2019).

scholarly journals Candidate gene prioritization using graph embedding

2020 ◽  
Author(s):  
Quan Do ◽  
Pierre Larmande
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Prediction Performance ◽  
Knowledge Graph ◽  
Learning Techniques ◽  
Number Of Genes ◽  
Important Amount ◽  
Candidate Gene Prioritization ◽  
Gene Information ◽  
Embedding Methods

AbstractCandidate genes prioritization allows to rank among a large number of genes, those that are strongly associated with a phenotype or a disease. Due to the important amount of data that needs to be integrate and analyse, gene-to-phenotype association is still a challenging task. In this paper, we evaluated a knowledge graph approach combined with embedding methods to overcome these challenges. We first introduced a dataset of rice genes created from several open-access databases. Then, we used the Translating Embedding model and Convolution Knowledge Base model, to vectorize gene information. Finally, we evaluated the results using link prediction performance and vectors representation using some unsupervised learning techniques.

Link prediction via layer relevance of multiplex networks

2017 ◽  
Vol 28 (08) ◽  
pp. 1750101 ◽  
Author(s):  
Yabing Yao ◽  
Ruisheng Zhang ◽  
Fan Yang ◽  
Yongna Yuan ◽  
Qingshuang Sun ◽  
...  
Keyword(s):  
Structural Properties ◽  
Link Prediction ◽  
Structural Information ◽  
Similarity Index ◽  
Single Layer ◽  
Structural Features ◽  
Prediction Performance ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Node Similarity

In complex networks, the existing link prediction methods primarily focus on the internal structural information derived from single-layer networks. However, the role of interlayer information is hardly recognized in multiplex networks, which provide more diverse structural features than single-layer networks. Actually, the structural properties and functions of one layer can affect that of other layers in multiplex networks. In this paper, the effect of interlayer structural properties on the link prediction performance is investigated in multiplex networks. By utilizing the intralayer and interlayer information, we propose a novel “Node Similarity Index” based on “Layer Relevance” (NSILR) of multiplex network for link prediction. The performance of NSILR index is validated on each layer of seven multiplex networks in real-world systems. Experimental results show that the NSILR index can significantly improve the prediction performance compared with the traditional methods, which only consider the intralayer information. Furthermore, the more relevant the layers are, the higher the performance is enhanced.

Low-Complexity and High-Accuracy Semi-blind Joint Channel and Symbol Estimation for Massive MIMO-OFDM

2018 ◽  
Vol 38 (3) ◽  
pp. 1114-1136 ◽  
Author(s):  
Ricardo Kehrle Miranda ◽  
João Paulo C. L. da Costa ◽  
Binghua Guo ◽  
André L. F. de Almeida ◽  
Giovanni Del Galdo ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Low Complexity ◽  
High Accuracy ◽  
Mimo Ofdm ◽  
Joint Channel

Detection of Botnet Based Attacks on Network

2018 ◽  
pp. 101-116
Author(s):  
Prachi
Keyword(s):  
Large Scale ◽  
Flow Analysis ◽  
Traffic Analysis ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Botnet Detection ◽  
Learning Techniques ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Traffic Flow Analysis

This chapter describes how with Botnets becoming more and more the leading cyber threat on the web nowadays, they also serve as the key platform for carrying out large-scale distributed attacks. Although a substantial amount of research in the fields of botnet detection and analysis, bot-masters inculcate new techniques to make them more sophisticated, destructive and hard to detect with the help of code encryption and obfuscation. This chapter proposes a new model to detect botnet behavior on the basis of traffic analysis and machine learning techniques. Traffic analysis behavior does not depend upon payload analysis so the proposed technique is immune to code encryption and other evasion techniques generally used by bot-masters. This chapter analyzes the benchmark datasets as well as real-time generated traffic to determine the feasibility of botnet detection using traffic flow analysis. Experimental results clearly indicate that a proposed model is able to classify the network traffic as a botnet or as normal traffic with a high accuracy and low false-positive rates.

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