Link Prediction Based on Local Information

2011 ◽  
Author(s):  
Yuxiao Dong ◽  
Qing Ke ◽  
Bai Wang ◽  
Bin Wu
Keyword(s):  
Link Prediction ◽  
Local Information

Link prediction based on local major path degree

2018 ◽  
Vol 32 (29) ◽  
pp. 1850348
Author(s):  
Xu-Hua Yang ◽  
Xuhua Yang ◽  
Fei Ling ◽  
Hai-Feng Zhang ◽  
Duan Zhang ◽  
...  
Keyword(s):  
Data Mining ◽  
Biological Networks ◽  
Link Prediction ◽  
Prediction Performance ◽  
Local Information ◽  
Network Nodes ◽  
And Topology ◽  
Similarity Indices ◽  
High Prediction ◽  
Similarity Information

Link prediction can estimate the probablity of the existence of an unknown or future edges between two arbitrary disconnected nodes (two seed nodes) in complex networks on the basis of information regarding network nodes, edges and topology. With the important practical value in many fields such as social networks, electronic commerce, data mining and biological networks, link prediction is attracting considerable attention from scientists in various fields. In this paper, we find that degree distribution and strength of two- and three-step local paths between two seed nodes can reveal effective similarity information between the two nodes. An index called local major path degree (LMPD) is proposed to estimate the probability of generating a link between two seed nodes. To indicate the efficiency of this algorithm, we compare it with nine well-known similarity indices based on local information in 12 real networks. Results show that the LMPD algorithm can achieve high prediction performance.

scholarly journals Performance of local information-based link prediction: a sampling perspective

2012 ◽  
Vol 45 (34) ◽  
pp. 345001 ◽  
Author(s):  
Jichang Zhao ◽  
Xu Feng ◽  
Li Dong ◽  
Xiao Liang ◽  
Ke Xu
Keyword(s):  
Link Prediction ◽  
Local Information

Link prediction based on the tie connection strength of common neighbor

2019 ◽  
Vol 30 (11) ◽  
pp. 1950089 ◽  
Author(s):  
Yujie Yang ◽  
Jianhua Zhang ◽  
Xuzhen Zhu ◽  
Jinming Ma ◽  
Xin Su
Keyword(s):  
Network Topology ◽  
Link Prediction ◽  
Free Parameter ◽  
Prediction Method ◽  
Large Degree ◽  
Local Information ◽  
Connection Strength ◽  
Common Neighbor ◽  
Multiple Paths ◽  
The Common

Traditional link prediction indices focus on the degree of the common neighbor and consider that the common neighbor with large degree contributes less to the similarity of two unconnected endpoints. Therefore, some of the local information-based methods only restrain the common neighbor with large degree for avoiding the influence dissipation. We find, however, if the large degree common neighbor connects with two unconnected endpoints through multiple paths simultaneously, these paths actually serve as transmission influences instead of dissipation. We regard these paths as the tie connection strength (TCS) of the common neighbor, and larger TCS can promote two unconnected endpoints to link with each other. Meanwhile, we notice that the similarity of node-pairs also relates to the network topology structure. Thus, in order to study the influences of TCS and the network structure on similarity, we introduce a free parameter and propose a novel link prediction method based on the TCS of the common neighbor. The experiment results on 12 real networks suggest that the proposed TCS index can improve the accuracy of link prediction.

Analyzing the Potential Influence of Shanghai Stock Market Based on Link Prediction Method

2017 ◽  
Vol 5 (5) ◽  
pp. 446-461 ◽  
Author(s):  
Hongxing Yao ◽  
Yunxia Lu
Keyword(s):  
Stock Market ◽  
Link Prediction ◽  
Stock Exchange ◽  
Similarity Index ◽  
Prediction Method ◽  
Local Information ◽  
Market Model ◽  
Potential Influence ◽  
Threshold Method ◽  
Structural Centrality

Abstract In this paper, we analyze the 180 stocks which have the potential influence on the Shanghai Stock Exchange (SSE). First, we use the stock closing prices from January 1, 2005 to June 19, 2015 to calculate logarithmic the correlation coefficient and then build the stock market model by threshold method. Secondly, according to different networks under different thresholds, we find out the potential influence stocks on the basis of local structural centrality. Finally, by comparing the accuracy of similarity index of the local information and path in the link prediction method, we demonstrate that there are best similarity index to predict the probability for nodes connection in the different stock networks.

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