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 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.

On the Temporal Network Analysis With Link Prediction

2018 ◽  
Author(s):  
Bin Wu ◽  
C. Steve Suh
Keyword(s):  
Link Prediction ◽  
Probabilistic Models ◽  
Factorization Method ◽  
Prediction Algorithm ◽  
Specific Information ◽  
Evolving Networks ◽  
Prediction Algorithms ◽  
Node Attribute ◽  
The Matrix ◽  
Maximum Likelihood Methods

Literature review shows that much effort has been given to model physical systems involving a large number of interacting constituents. As a network evolves its constituents (or nodes) and associated links would either increase or decrease or both. It is a challenge to extract the specifics that underlie the evolution of a network or indicate the addition and/or removal of links in time. Similarity-based algorithm, Maximum likelihood methods, and Probabilistic models are 3 mainstream methods for link prediction. Methods incorporating topological feature and node attribute are shown to be more effective than most strategies for link prediction. However, to improve prediction accuracy, an effective prediction strategy of practicality is still being sought that captures the characteristics fundamental to a complex system. Many link prediction algorithms have been developed that handle large networks of complexity. These algorithms usually assume that a network is static. They are also computationally inefficient. All these limitations inevitably lead to poor predictions. This paper addresses the link prediction problem by incorporating microscopic dynamics into the matrix factorization method to extract specific information from a time-evolving network with improved link prediction. Numerical experiments in applying static methods to temporal networks show that existing link prediction algorithms all demonstrate unsatisfactory performances in link prediction, thus suggesting that a new prediction algorithm viable for time-evolving networks is required.

Efficient incremental dynamic link prediction algorithms in social network

2017 ◽  
Vol 132 ◽  
pp. 226-235 ◽  
Author(s):  
Zhongbao Zhang ◽  
Jian Wen ◽  
Li Sun ◽  
Qiaoyu Deng ◽  
Sen Su ◽  
...  
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Prediction Algorithms ◽  
Dynamic Link

scholarly journals Comparison Analysis of Link Prediction Algorithms in Social Network

2015 ◽  
Vol 111 (16) ◽  
pp. 27-29 ◽  
Author(s):  
Sahil Gupta ◽  
Shalini Pandey ◽  
K.K.Shukla K.K.Shukla
Keyword(s):  
Social Network ◽  
Link Prediction ◽  
Comparison Analysis ◽  
Prediction Algorithms

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

scholarly journals Deep Reinforcement Learning Based on Link Prediction Method in Social Network Analysis

2019 ◽  
Vol 9 (2S2) ◽  
pp. 820-826
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Link Prediction ◽  
Prediction Models ◽  
Learning Strategy ◽  
Prediction Method ◽  
Gradient Boosting ◽  
Data Set

Improving the performance of link prediction is a significant role in the evaluation of social network. Link prediction is known as one of the primary purposes for recommended systems, bio information, and web. Most machine learning methods that depend on SNA model’s metrics use supervised learning to develop link prediction models. Supervised learning actually needed huge amount of data set to train the model of link prediction to obtain an optimal level of performance. In few years, Deep Reinforcement Learning (DRL) has achieved excellent success in various domain such as SNA. In this paper, we present the use of deep reinforcement learning (DRL) to improve the performance and accuracy of the model for the applied dataset. The experiment shows that the dataset created by the DRL model through self-play or auto-simulation can be utilized to improve the link prediction model. We have used three different datasets: JUNANES, MAMBO, JAKE. Experimental results show that the DRL proposed method provide accuracy of 85% for JUNANES, 87% for MAMABO, and 78% for JAKE dataset which outperforms the GBM next highest accuracy of 75% for JUNANES, 79% for MAMBO and 71% for JAKE dataset respectively trained with 2500 iteration and also in terms of AUC measures as well. The DRL model shows the better efficiency than a traditional machine learning strategy, such as, Random Forest and the gradient boosting machine (GBM).

Constructing the social network prediction model based on data mining and link prediction analysis

2019 ◽  
Vol 38 (2) ◽  
pp. 320-333
Author(s):  
Yuxian Gao
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Link Prediction ◽  
Prediction Method ◽  
Analysis Method ◽  
Data Set ◽  
Content Type ◽  
Community Mining ◽  
The Relationship

Purpose The purpose of this paper is to apply link prediction to community mining and to clarify the role of link prediction in improving the performance of social network analysis. Design/methodology/approach In this study, the 2009 version of Enron e-mail data set provided by Carnegie Mellon University was selected as the research object first, and bibliometric analysis method and citation analysis method were adopted to compare the differences between various studies. Second, based on the impact of various interpersonal relationships, the link model was adopted to analyze the relationship among people. Finally, the factorization of the matrix was further adopted to obtain the characteristics of the research object, so as to predict the unknown relationship. Findings The experimental results show that the prediction results obtained by considering multiple relationships are more accurate than those obtained by considering only one relationship. Research limitations/implications Due to the limited number of objects in the data set, the link prediction method has not been tested on the large-scale data set, and the validity and correctness of the method need to be further verified with larger data. In addition, the research on algorithm complexity and algorithm optimization, including the storage of sparse matrix, also need to be further studied. At the same time, in the case of extremely sparse data, the accuracy of the link prediction method will decline a lot, and further research and discussion should be carried out on the sparse data. Practical implications The focus of this research is on link prediction in social network analysis. The traditional prediction model is based on a certain relationship between the objects to predict and analyze, but in real life, the relationship between people is diverse, and different relationships are interactive. Therefore, in this study, the graph model is used to express different kinds of relations, and the influence between different kinds of relations is considered in the actual prediction process. Finally, experiments on real data sets prove the effectiveness and accuracy of this method. In addition, link prediction, as an important part of social network analysis, is also of great significance for other applications of social network analysis. This study attempts to prove that link prediction is helpful to the improvement of performance analysis of social network by applying link prediction to community mining. Originality/value This study adopts a variety of methods, such as link prediction, data mining, literature analysis and citation analysis. The research direction is relatively new, and the experimental results obtained have a certain degree of credibility, which is of certain reference value for the following related research.

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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