Weighted One Mode Projection of a Bipartite Graph as a Local Similarity Measure

2017 ◽  
pp. 375-389 ◽  
Author(s):  
Rotem Stram ◽  
Pascal Reuss ◽  
Klaus-Dieter Althoff
Keyword(s):  
Bipartite Graph ◽  
Similarity Measure ◽  
Local Similarity

scholarly journals Change detection using a local similarity measure

2008 ◽  
Author(s):  
M. Jahari ◽  
S. Khairunniza-Bejo ◽  
A. R. M. Shariff ◽  
H. Z. M. Shafri ◽  
H. Ibrahim
Keyword(s):  
Change Detection ◽  
Similarity Measure ◽  
Local Similarity

A Power Network Partitioning Method Based on Local Similarity Measure

2012 ◽  
Vol 588-589 ◽  
pp. 364-367
Author(s):  
Tao Wang ◽  
Heng Zhou ◽  
Pan Zou
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Reactive Power ◽  
Graph Model ◽  
Weighted Graph ◽  
Local Similarity ◽  
Network Partitioning ◽  
Power Network ◽  
Clustering Method ◽  
Partitioning Algorithm

A power network partitioning model based on the weighed local similarity measure is presented in this paper considering the regional decoupling characteristics of reactive power. A weighted graph model of reactive power network is established and a new measurement of local similarity based on weighed graph is defined. To utilize our measurement of similarity to partition reactive power network, a partitioning algorithm based on generalized ward hierarchical clustering method is proposed. The algorithm can ensure balance of the reactive power inside partition. Applying the proposed algorithm to IEEE 39-bus system, the results show that the proposed algorithm is feasible and effective.

scholarly journals Bilateral Weighted Adaptive Local Similarity Measure for Registration in Neurosurgery

2016 ◽  
pp. 81-88
Author(s):  
Martin Kochan ◽  
Marc Modat ◽  
Tom Vercauteren ◽  
Mark White ◽  
Laura Mancini ◽  
...  
Keyword(s):  
Similarity Measure ◽  
Local Similarity

Efficient motion data indexing and retrieval with local similarity measure of motion strings

2009 ◽  
Vol 25 (5-7) ◽  
pp. 499-508 ◽  
Author(s):  
Shuangyuan Wu ◽  
Shihong Xia ◽  
Zhaoqi Wang ◽  
Chunpeng Li
Keyword(s):  
Similarity Measure ◽  
Local Similarity ◽  
Data Indexing ◽  
Motion Data ◽  
Indexing And Retrieval

scholarly journals A Novel Similarity Measure of Link Prediction in Multi-Layer Social Networks Based on Reliable Paths

2021 ◽  
Author(s):  
Amin Rezaeipanah
Keyword(s):  
Social Networks ◽  
Similarity Measure ◽  
Network Structure ◽  
Online Social Networks ◽  
Link Prediction ◽  
Similarity Measures ◽  
Single Layer ◽  
Superior Performance ◽  
Local Similarity ◽  
Topological Features

Abstract Online social networks are an integral element of modern societies and significantly influence the formation and consolidation of social relationships. In fact, these networks are multi-layered so that there may be multiple links between a user’ on different social networks. In this paper, the link prediction problem for the same user in a two-layer social network is examined, where we consider Twitter and Foursquare networks. Here, information related to the two-layer communication is used to predict links in the Foursquare network. Link prediction aims to discover spurious links or predict the emergence of future links from the current network structure. There are many algorithms for link prediction in unweighted networks, however only a few have been developed for weighted networks. Based on the extraction of topological features from the network structure and the use of reliable paths between users, we developed a novel similarity measure for link prediction. Reliable paths have been proposed to develop unweight local similarity measures to weighted measures. Using these measures, both the existence of links and their weight can be predicted. Empirical analysis shows that the proposed similarity measure achieves superior performance to existing approaches and can more accurately predict future relationships. In addition, the proposed method has better results compared to single-layer networks. Experiments show that the proposed similarity measure has an advantage precision of 1.8% over the Katz and FriendLink measures.

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