Research on Social Network Link Prediction Algorithm Based on Multidimensional Similarity Attributes

2018 ◽  
Vol 08 (08) ◽  
pp. 1239-1258
Author(s):  
伟杰 杨
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Prediction Algorithm ◽  
Network Link

scholarly journals Group Relationship Mining of College Students Based on Predictive Social Network

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Huazhang Liu
Keyword(s):  
College Students ◽  
Social Networks ◽  
Social Network ◽  
Complex Network ◽  
Link Prediction ◽  
Rapid Development ◽  
Combination Method ◽  
Prediction Algorithm ◽  
The Social ◽  
Random Block

With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students’ group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students’ group relationships, the classification of college students’ groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students’ networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students’ association classification.

scholarly journals The Algorithm of Link Prediction on Social Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Liyan Dong ◽  
Yongli Li ◽  
Han Yin ◽  
Huang Le ◽  
Mao Rui
Keyword(s):  
Social Network ◽  
Network Topology ◽  
Link Prediction ◽  
Prediction Algorithm ◽  
Similarity Function ◽  
Information Network ◽  
Global Information ◽  
Data Set ◽  
Prediction Algorithms ◽  
Topology Information

At present, most link prediction algorithms are based on the similarity between two entities. Social network topology information is one of the main sources to design the similarity function between entities. But the existing link prediction algorithms do not apply the network topology information sufficiently. For lack of traditional link prediction algorithms, we propose two improved algorithms: CNGF algorithm based on local information and KatzGF algorithm based on global information network. For the defect of the stationary of social network, we also provide the link prediction algorithm based on nodes multiple attributes information. Finally, we verified these algorithms on DBLP data set, and the experimental results show that the performance of the improved algorithm is superior to that of the traditional link prediction algorithm.

scholarly journals A Weight based Scheme for Improving the Accuracy of Relationship in Social Network

2019 ◽  
Vol 8 (11) ◽  
pp. 3040-3043 ◽  
Keyword(s):  
Social Media ◽  
Social Network ◽  
Link Prediction ◽  
Performance Metrics ◽  
Prediction Algorithm ◽  
Relationship Matrix ◽  
Community Network ◽  
The Social ◽  
Relationship Value ◽  
The Relationship

The usage of social media has become unavoidable in the last decade. The social media is highly dynamic in nature and grows rapidly. The community network offers a rich expedient of various data. The detection of communities is based on the frequency in the networks which is usually represented by graphs. The vertices (nodes) are representing the social actor and the edges (links) represent the relation between those actors. The community link detection is as hard as the graph increases up to millions of vertices and edges. The accuracy of link prediction for inferring missing (erased or broken) links is very complex due to the dynamic nature of links. The links are updated from time to time and the new links are established dynamically. As the links are appeared and disappeared dynamically, the accuracy of identifying the edges of the social network graph of the user is complex in nature. Many efforts have been put up in developing link prediction algorithms in the past, but still there is a lacuna in accuracy in predicting inferred / broken links. A weight based link prediction algorithm is proposed to improve the accuracy of the link prediction on inferred / broken links in the social media. In this method, a weight based link analysis is employed to quantify the relative value between two nodes in the community network. The correlation value for relationship is also determined over a period of time using the designed relationship matrix. The relationship value between the nodes is computed by a Euclidian distance approach. The relationship value of each node is determined by the relationship equation using weight values. The proposed approach is experimented in constrained environment for 2 users’ Facebook usages over a period of a year. The accuracy of relationship is used as performance metrics. The results shown that the accuracy is improved 2.35% more than random predictor method

Performance Analysis of Naïve Bayes Classifier Over Similarity Score-Based Techniques for Missing Link Prediction in Ego Networks

2021 ◽  
Vol 14 (1) ◽  
pp. 110-122
Author(s):  
Anand Kumar Gupta ◽  
Neetu Sardana
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Performance Analysis ◽  
Link Prediction ◽  
Similarity Score ◽  
Learning Performance ◽  
Bayes Classifier ◽  
Missing Link ◽  
Ego Networks ◽  
Network Link

Keywords Ego Network, Link Prediction, Machine Learning, Performance Analysis, Similarity Score, Social Network

scholarly journals Higher-order temporal network effects through triplet evolution

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Yao ◽  
Bingsheng Chen ◽  
Tim S. Evans ◽  
Kim Christensen
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Higher Order ◽  
Prediction Algorithm ◽  
Interaction Patterns ◽  
Temporal Networks ◽  
World Systems ◽  
Order Interaction ◽  
Space And Time ◽  
Pairwise Interactions

AbstractWe study the evolution of networks through ‘triplets’—three-node graphlets. We develop a method to compute a transition matrix to describe the evolution of triplets in temporal networks. To identify the importance of higher-order interactions in the evolution of networks, we compare both artificial and real-world data to a model based on pairwise interactions only. The significant differences between the computed matrix and the calculated matrix from the fitted parameters demonstrate that non-pairwise interactions exist for various real-world systems in space and time, such as our data sets. Furthermore, this also reveals that different patterns of higher-order interaction are involved in different real-world situations. To test our approach, we then use these transition matrices as the basis of a link prediction algorithm. We investigate our algorithm’s performance on four temporal networks, comparing our approach against ten other link prediction methods. Our results show that higher-order interactions in both space and time play a crucial role in the evolution of networks as we find our method, along with two other methods based on non-local interactions, give the best overall performance. The results also confirm the concept that the higher-order interaction patterns, i.e., triplet dynamics, can help us understand and predict the evolution of different real-world systems.

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