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Showing 1–12 of 12 results
Advanced filters: Author: Alec Kirkley Clear advanced filters

  • From blood tests to friend groups, normalized mutual information is widely used to assess similarity between classifications, outcomes, or labelings of data. Here the authors demonstrate systematic biases of this measure and propose a modification that eliminates them.

    • Maximilian Jerdee
    • Alec Kirkley
    • Mark Newman
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-8

  • Ranking items based on pairwise comparisons, such as using match outcomes to rank sports teams, is a common task that becomes challenging when data is limited or noisy. Here, the authors introduce an efficient nonparametric Bayesian method for learning partial rankings that breaks ties among item ranks only when supported by sufficient statistical evidence in the data.

    • Sebastian Morel-Balbi
    • Alec Kirkley
    ResearchOpen Access
    Communications Physics
    Volume: 9, P: 1-15

  • The betweenness centrality is a metric commonly used in network analysis. Here the authors show that the distribution of this metric in urban street networks is invariant in the case of 97 cities. This invariance could affect network flows, dynamics and congestion management in cities.

    • Alec Kirkley
    • Hugo Barbosa
    • Gourab Ghoshal
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-12

  • Graph similarity measures are essential for downstream tasks including clustering, embedding, and regression with populations of networks. Here the authors derive a family of graph mutual information measures that allow for a principled, interpretable, and efficient comparison of networks at multiple scales.

    • Helcio Felippe
    • Federico Battiston
    • Alec Kirkley
    ResearchOpen Access
    Communications Physics
    Volume: 7, P: 1-12

  • In many experimental settings, one repeatedly observes the connectivity of a set of nodes to form a population of networks or multilayer networks. The authors present nonparametric, information-theoretic methods to automatically construct representative “modal” networks that parsimoniously capture the variety of structures present in these measurements.

    • Alec Kirkley
    • Alexis Rojas
    • Jean-Gabriel Young
    ResearchOpen Access
    Communications Physics
    Volume: 6, P: 1-10

  • Aggregating fine-grained data allows for the reduction of the impact of noise and outliers when analysing real-world systems. Here the author proposes a principled and efficient method based on information theory to compress spatial systems into macroscopic regions, capturing spatial clusters able to reveal the complex spatial organization of socioeconomic systems and demographic data.

    • Alec Kirkley
    ResearchOpen Access
    Communications Physics
    Volume: 5, P: 1-10

  • Community detection is a common task in the analysis of network data but most current community detection algorithms provide either only a single partition or a very large number of plausible ones, neither of which gives an interpretable summary of the possible structures. Here the authors provide a solution to this problem, in the form of an algorithm based on the minimum description length principle that identifies minimal sets of archetypal, highly representative partitions of a network that succinctly summarize the plausible community structures.

    • Alec Kirkley
    • M. E. J. Newman
    ResearchOpen Access
    Communications Physics
    Volume: 5, P: 1-10