The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds man...The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds many practical applications in numerous areas such as information dissemination,epidemic immunity,and viral marketing.However,most existing influence maximization algorithms are limited by the“rich-club”phenomenon and are thus unable to avoid the influence overlap of seed spreaders.This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy,named AIGCrank,to identify a set of influential seeds.Specifically,the gravity centrality jointly employs the neighborhood,network location and topological structure information of nodes to evaluate each node's potential of being selected as a seed.We also present a recursive ranking strategy for identifying seed nodes one-byone.Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.展开更多
Traditional information retrieval systems respond to user queries with ranked lists of relevant documents. Since, XML （Extensible Markup Language） documents separate content and structure; XML-IR （information retri...Traditional information retrieval systems respond to user queries with ranked lists of relevant documents. Since, XML （Extensible Markup Language） documents separate content and structure; XML-IR （information retrieval） systems are able to retrieve only the relevant portions of documents. Therefore, users who utilize an XML-IR system could potentially receive highly relevant and precise material. We have developed the XML information retrieval system by using MySQL and Sphinx, which we call MEXIR. In our system, XML documents are stored into one table that has fixed relational schema. The schema is independent of the logical structure of XML documents. Each node in XML documents is represented by labels that express the positions in XML tree, namely ADXPI scheme. Our system has performance experiments on INEX collections and shown an average up to four seconds better than GPX. In addition, it has been reduced the size of the data down by 82.29 % compare to GPX system.展开更多
基金the National Social Science Foundation of China(Grant Nos.21BGL217 and 18AZD005)the National Natural Science Foundation of China(Grant Nos.71874108 and 11871328)。
文摘The influence maximization problem in complex networks asks to identify a given size of seed spreaders set to maximize the number of expected influenced nodes at the end of the spreading process.This problem finds many practical applications in numerous areas such as information dissemination,epidemic immunity,and viral marketing.However,most existing influence maximization algorithms are limited by the“rich-club”phenomenon and are thus unable to avoid the influence overlap of seed spreaders.This work proposes a novel adaptive algorithm based on a new gravity centrality and a recursive ranking strategy,named AIGCrank,to identify a set of influential seeds.Specifically,the gravity centrality jointly employs the neighborhood,network location and topological structure information of nodes to evaluate each node's potential of being selected as a seed.We also present a recursive ranking strategy for identifying seed nodes one-byone.Experimental results show that our algorithm competes very favorably with the state-of-the-art algorithms in terms of influence propagation and coverage redundancy of the seed set.
文摘Traditional information retrieval systems respond to user queries with ranked lists of relevant documents. Since, XML （Extensible Markup Language） documents separate content and structure; XML-IR （information retrieval） systems are able to retrieve only the relevant portions of documents. Therefore, users who utilize an XML-IR system could potentially receive highly relevant and precise material. We have developed the XML information retrieval system by using MySQL and Sphinx, which we call MEXIR. In our system, XML documents are stored into one table that has fixed relational schema. The schema is independent of the logical structure of XML documents. Each node in XML documents is represented by labels that express the positions in XML tree, namely ADXPI scheme. Our system has performance experiments on INEX collections and shown an average up to four seconds better than GPX. In addition, it has been reduced the size of the data down by 82.29 % compare to GPX system.
