Network information mining is the study of the network topology,which may answer a large number of applicationbased questions towards the structural evolution and the function of a real system.The question can be rela...Network information mining is the study of the network topology,which may answer a large number of applicationbased questions towards the structural evolution and the function of a real system.The question can be related to how the real system evolves or how individuals interact with each other in social networks.Although the evolution of the real system may seem to be found regularly,capturing patterns on the whole process of evolution is not trivial.Link prediction is one of the most important technologies in network information mining,which can help us understand the evolution mechanism of real-life network.Link prediction aims to uncover missing links or quantify the likelihood of the emergence of nonexistent links from known network structures.Currently,widely existing methods of link prediction almost focus on short-path networks that usually have a myriad of close triangular structures.However,these algorithms on highly sparse or longpath networks have poor performance.Here,we propose a new index that is associated with the principles of structural equivalence and shortest path length(SESPL)to estimate the likelihood of link existence in long-path networks.Through a test of 548 real networks,we find that SESPL is more effective and efficient than other similarity-based predictors in long-path networks.Meanwhile,we also exploit the performance of SESPL predictor and of embedding-based approaches via machine learning techniques.The results show that the performance of SESPL can achieve a gain of 44.09%over GraphWave and 7.93%over Node2vec.Finally,according to the matrix of maximal information coefficient(MIC)between all the similarity-based predictors,SESPL is a new independent feature in the space of traditional similarity features.展开更多
Based on the model of the same degree of all nodes we proposed before, a new algorithm, the so-called “spread all over vertices” （SAV） algorithm, is proposed for generating small-world properties from a regular ri...Based on the model of the same degree of all nodes we proposed before, a new algorithm, the so-called “spread all over vertices” （SAV） algorithm, is proposed for generating small-world properties from a regular ring lattices. During randomly rewiring connections the SAV is used to keep the unchanged number of links. Comparing the SAV algorithm with the Watts-Strogatz model and the “spread all over boundaries” algorithm, three methods can have the same topological properties of the small world networks. These results offer diverse formation of small world networks. It is helpful to the research of some applications for dynamics of mutual oscillator inside nodes and interacting automata associated with networks.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61773091 and 62173065)the Industry-University-Research Innovation Fund for Chinese Universities(Grant No.2021ALA03016)+2 种基金the Fund for University Innovation Research Group of Chongqing(Grant No.CXQT21005)the National Social Science Foundation of China(Grant No.20CTQ029)the Fundamental Research Funds for the Central Universities(Grant No.SWU119062).
文摘Network information mining is the study of the network topology,which may answer a large number of applicationbased questions towards the structural evolution and the function of a real system.The question can be related to how the real system evolves or how individuals interact with each other in social networks.Although the evolution of the real system may seem to be found regularly,capturing patterns on the whole process of evolution is not trivial.Link prediction is one of the most important technologies in network information mining,which can help us understand the evolution mechanism of real-life network.Link prediction aims to uncover missing links or quantify the likelihood of the emergence of nonexistent links from known network structures.Currently,widely existing methods of link prediction almost focus on short-path networks that usually have a myriad of close triangular structures.However,these algorithms on highly sparse or longpath networks have poor performance.Here,we propose a new index that is associated with the principles of structural equivalence and shortest path length(SESPL)to estimate the likelihood of link existence in long-path networks.Through a test of 548 real networks,we find that SESPL is more effective and efficient than other similarity-based predictors in long-path networks.Meanwhile,we also exploit the performance of SESPL predictor and of embedding-based approaches via machine learning techniques.The results show that the performance of SESPL can achieve a gain of 44.09%over GraphWave and 7.93%over Node2vec.Finally,according to the matrix of maximal information coefficient(MIC)between all the similarity-based predictors,SESPL is a new independent feature in the space of traditional similarity features.
基金The project supported by the Key Project5 of National Natural Science Foundation of China under Grant No 70431002, and National Natural Science Foundation of China under Grant Nos. 70371068 and 10247005
文摘Based on the model of the same degree of all nodes we proposed before, a new algorithm, the so-called “spread all over vertices” （SAV） algorithm, is proposed for generating small-world properties from a regular ring lattices. During randomly rewiring connections the SAV is used to keep the unchanged number of links. Comparing the SAV algorithm with the Watts-Strogatz model and the “spread all over boundaries” algorithm, three methods can have the same topological properties of the small world networks. These results offer diverse formation of small world networks. It is helpful to the research of some applications for dynamics of mutual oscillator inside nodes and interacting automata associated with networks.
