The Experts below are selected from a list of 1206 Experts worldwide ranked by ideXlab platform

Ali Jadbabaie - One of the best experts on this subject based on the ideXlab platform.

  • CDC - Small world Phenomenon, rapidly mixing Markov chains, and average consensus algorithms
    2007 46th IEEE Conference on Decision and Control, 2007
    Co-Authors: Alireza Tahbaz-salehi, Ali Jadbabaie
    Abstract:

    In this paper, we demonstrate the relationship between the diameter of a graph and the mixing time of a symmetric Markov chain defined on it. We use this relationship to show that graphs with the small world property have dramatically small mixing times. Based on this result, we conclude that addition of independent random edges with arbitrarily small probabilities to a cycle significantly increases the convergence speed of average consensus algorithms, meaning that small world networks reach consensus orders of magnitude faster than a cycle. Furthermore, this dramatic increase happens for any positive probability of random edges. The same argument is used to draw a similar conclusion for the case of addition of a random matching to the cycle.

  • Small world Phenomenon, rapidly mixing Markov chains, and average consensus algorithms
    2007 46th IEEE Conference on Decision and Control, 2007
    Co-Authors: Alireza Tahbaz-salehi, Ali Jadbabaie
    Abstract:

    In this paper, we demonstrate the relationship between the diameter of a graph and the mixing time of a symmetric Markov chain defined on it. We use this relationship to show that graphs with the small world property have dramatically small mixing times. Based on this result, we conclude that addition of independent random edges with arbitrarily small probabilities to a cycle significantly increases the convergence speed of average consensus algorithms, meaning that small world networks reach consensus orders of magnitude faster than a cycle. Furthermore, this dramatic increase happens for any positive probability of random edges. The same argument is used to draw a similar conclusion for the case of addition of a random matching to the cycle.

Alireza Tahbaz-salehi - One of the best experts on this subject based on the ideXlab platform.

  • CDC - Small world Phenomenon, rapidly mixing Markov chains, and average consensus algorithms
    2007 46th IEEE Conference on Decision and Control, 2007
    Co-Authors: Alireza Tahbaz-salehi, Ali Jadbabaie
    Abstract:

    In this paper, we demonstrate the relationship between the diameter of a graph and the mixing time of a symmetric Markov chain defined on it. We use this relationship to show that graphs with the small world property have dramatically small mixing times. Based on this result, we conclude that addition of independent random edges with arbitrarily small probabilities to a cycle significantly increases the convergence speed of average consensus algorithms, meaning that small world networks reach consensus orders of magnitude faster than a cycle. Furthermore, this dramatic increase happens for any positive probability of random edges. The same argument is used to draw a similar conclusion for the case of addition of a random matching to the cycle.

  • Small world Phenomenon, rapidly mixing Markov chains, and average consensus algorithms
    2007 46th IEEE Conference on Decision and Control, 2007
    Co-Authors: Alireza Tahbaz-salehi, Ali Jadbabaie
    Abstract:

    In this paper, we demonstrate the relationship between the diameter of a graph and the mixing time of a symmetric Markov chain defined on it. We use this relationship to show that graphs with the small world property have dramatically small mixing times. Based on this result, we conclude that addition of independent random edges with arbitrarily small probabilities to a cycle significantly increases the convergence speed of average consensus algorithms, meaning that small world networks reach consensus orders of magnitude faster than a cycle. Furthermore, this dramatic increase happens for any positive probability of random edges. The same argument is used to draw a similar conclusion for the case of addition of a random matching to the cycle.

Tiejun Huang - One of the best experts on this subject based on the ideXlab platform.

  • Semantic Scoring Based on Small-World Phenomenon for Feature Selection in Text Mining
    Lecture Notes in Computer Science, 2020
    Co-Authors: Chong Huang, Yonghong Tian, Tiejun Huang
    Abstract:

    This paper proposes an effective scoring scheme for feature selection in Text Mining, using characteristics of Small-World Phenomenon on the semantic networks of documents. Our focus is on the reservation of both syntactic and statistical information of words, rather than solely simple frequency summarization in prevailing scoring schemes, such as TFIDF. Experimental results on TREC dataset show that our scoring scheme outperforms the prevailing schemes.

  • ADMA - Semantic scoring based on Small-World Phenomenon for feature selection in text mining
    Advanced Data Mining and Applications, 2006
    Co-Authors: Chong Huang, Yonghong Tian, Tiejun Huang
    Abstract:

    This paper proposes an effective scoring scheme for feature selection in Text Mining, using characteristics of Small-World Phenomenon on the semantic networks of documents. Our focus is on the reservation of both syntactic and statistical information of words, rather than solely simple frequency summarization in prevailing scoring schemes, such as TFIDF. Experimental results on TREC dataset show that our scoring scheme outperforms the prevailing schemes.

Chong Huang - One of the best experts on this subject based on the ideXlab platform.

  • Semantic Scoring Based on Small-World Phenomenon for Feature Selection in Text Mining
    Lecture Notes in Computer Science, 2020
    Co-Authors: Chong Huang, Yonghong Tian, Tiejun Huang
    Abstract:

    This paper proposes an effective scoring scheme for feature selection in Text Mining, using characteristics of Small-World Phenomenon on the semantic networks of documents. Our focus is on the reservation of both syntactic and statistical information of words, rather than solely simple frequency summarization in prevailing scoring schemes, such as TFIDF. Experimental results on TREC dataset show that our scoring scheme outperforms the prevailing schemes.

  • ADMA - Semantic scoring based on Small-World Phenomenon for feature selection in text mining
    Advanced Data Mining and Applications, 2006
    Co-Authors: Chong Huang, Yonghong Tian, Tiejun Huang
    Abstract:

    This paper proposes an effective scoring scheme for feature selection in Text Mining, using characteristics of Small-World Phenomenon on the semantic networks of documents. Our focus is on the reservation of both syntactic and statistical information of words, rather than solely simple frequency summarization in prevailing scoring schemes, such as TFIDF. Experimental results on TREC dataset show that our scoring scheme outperforms the prevailing schemes.

João Seixas - One of the best experts on this subject based on the ideXlab platform.

  • A dynamical characterization of the small world phase
    Physics Letters A, 2020
    Co-Authors: Tanya Araújo, R. Vilela Mendes, João Seixas
    Abstract:

    Small-World (SW) networks have been identified in many different fields. Topological coefficients like the clustering coefficient and the characteristic path length have been used in the past for a qualitative characterization of these networks. Here a dynamical approach is used to characterize the Small-World Phenomenon. Using the $\beta -$model, a coupled map dynamical system is defined on the network. Entrance to and exit from the SW phase are related to the behavior of the ergodic invariants of the dynamics.Comment: 8 pages Latex, 3 figure

  • A dynamical characterization of the small world phase
    Physics Letters A, 2003
    Co-Authors: Tanya Araújo, R. Vilela Mendes, João Seixas
    Abstract:

    Abstract Small-World (SW) networks have been identified in many different fields. Topological coefficients like the clustering coefficient and the characteristic path length have been used in the past for a qualitative characterization of these networks. Here a dynamical approach is used to characterize the Small-World Phenomenon. Using the Watts–Strogatz β -model, a coupled map dynamical system is defined on the network. Entrance to and exit from the SW phase are related to the behavior of the ergodic invariants of the dynamics.