A multidimensional network link prediction algorithm and its application for predicting social relationships

2021 ◽  
Vol 53 ◽  
pp. 101358
Author(s):  
Guanghui Wang ◽  
Yufei Wang ◽  
Jimei Li ◽  
Kaidi Liu
Keyword(s):  
Social Relationships ◽  
Link Prediction ◽  
Prediction Algorithm ◽  
Network Link

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.

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.

Domain Knowledge-Based Link Prediction in Customer-Product Bipartite Graph for Product Recommendation

2019 ◽  
Vol 18 (01) ◽  
pp. 311-338 ◽  
Author(s):  
Lingling Zhang ◽  
Jing Li ◽  
Qiuliu Zhang ◽  
Fan Meng ◽  
Weili Teng
Keyword(s):  
Bipartite Graph ◽  
Link Prediction ◽  
Domain Knowledge ◽  
Attenuation Factor ◽  
Prediction Algorithm ◽  
Bipartite Network ◽  
Topological Similarity ◽  
Product Recommendation ◽  
Knowledge Based ◽  
Context Knowledge

In this paper, we propose domain knowledge-based link prediction algorithm in customer-product bipartite network to improve effectiveness of product recommendation in retail. The domain knowledge is classified into product domain knowledge and time context knowledge, which play an important part in link prediction. We take both of them into consideration in recommendation and form a unified domain knowledge-based link prediction framework. We capture product semantic similarity by ontology-based analysis and time attenuation factor from time context knowledge, then incorporate them into network topological similarity to form a new linkage measure. To evaluate the algorithm, we use a real retail transaction dataset from Food Mart. Experimental results demonstrate that the usage of domain knowledge in link prediction achieved significantly better performance.

Link prediction based on network embedding and similarity transferring methods

2020 ◽  
Vol 34 (16) ◽  
pp. 2050169
Author(s):  
Wei Yu ◽  
Xiaoyu Liu ◽  
Bo Ouyang
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Free Parameter ◽  
Network Science ◽  
Ad Hoc ◽  
Prediction Algorithm ◽  
Network Embedding ◽  
Science Community ◽  
The Cost ◽  
Accuracy Of Prediction

In network science, link prediction is a technique used to predict missing or future relationships based on currently observed connections. Much attention from the network science community is paid to this direction recently. However, most present approaches predict links based on ad hoc similarity definitions. To address this issue, we propose a link prediction algorithm named Transferring Similarity Based on Adjacency Embedding (TSBAE). TSBAE is based on network embedding, where the potential information of the structure is preserved in the embedded vector space, and the similarity is inherently captured by the distance of these vectors. Furthermore, to accommodate the fact that the similarity should be transferable, indirect similarity between nodes is incorporated to improve the accuracy of prediction. The experimental results on 10 real-world networks show that TSBAE outperforms the baseline algorithms in the task of link prediction, with the cost of tuning a free parameter in the prediction.

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.

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