методы анализа сетей - Network histograms
Новая статья в последнем номере PNAS о новом методе анализа взаимодействий в сетях, в том числе социальных. В качестве иллюстрации метода используют сети политических блогов и сети дружбы среди студентов.
Статья в свободном доступе.
Как-то так и должен выглядеть анализ общества, о котором я иногда упоминаю в своих постах - анализ этот должен использовать самые современные методы, которые максимально учитывают разветвленность системы.
Коды методики на Matlab'е в свободном доступе по ссылке в статье.
Network histograms and universality of blockmodel approximation
http://www.pnas.org/content/111/41/14722.abstract.html?etoc
Abstract
In this paper we introduce the network histogram, a statistical summary of network interactions to be used as a tool for exploratory data analysis. A network histogram is obtained by fitting a stochastic blockmodel to a single observation of a network dataset. Blocks of edges play the role of histogram bins and community sizes that of histogram bandwidths or bin sizes. Just as standard histograms allow for varying bandwidths, different blockmodel estimates can all be considered valid representations of an underlying probability model, subject to bandwidth constraints. Here we provide methods for automatic bandwidth selection, by which the network histogram approximates the generating mechanism that gives rise to exchangeable random graphs. This makes the blockmodel a universal network representation for unlabeled graphs. With this insight, we discuss the interpretation of network communities in light of the fact that many different community assignments can all give an equally valid representation of such a network. To demonstrate the fidelity-versus-interpretability tradeoff inherent in considering different numbers and sizes of communities, we analyze two publicly available networks-political weblogs and student friendships-and discuss how to interpret the network histogram when additional information related to node and edge labeling is present.
Статья в свободном доступе.
Как-то так и должен выглядеть анализ общества, о котором я иногда упоминаю в своих постах - анализ этот должен использовать самые современные методы, которые максимально учитывают разветвленность системы.
Коды методики на Matlab'е в свободном доступе по ссылке в статье.
Network histograms and universality of blockmodel approximation
http://www.pnas.org/content/111/41/14722.abstract.html?etoc
Abstract
In this paper we introduce the network histogram, a statistical summary of network interactions to be used as a tool for exploratory data analysis. A network histogram is obtained by fitting a stochastic blockmodel to a single observation of a network dataset. Blocks of edges play the role of histogram bins and community sizes that of histogram bandwidths or bin sizes. Just as standard histograms allow for varying bandwidths, different blockmodel estimates can all be considered valid representations of an underlying probability model, subject to bandwidth constraints. Here we provide methods for automatic bandwidth selection, by which the network histogram approximates the generating mechanism that gives rise to exchangeable random graphs. This makes the blockmodel a universal network representation for unlabeled graphs. With this insight, we discuss the interpretation of network communities in light of the fact that many different community assignments can all give an equally valid representation of such a network. To demonstrate the fidelity-versus-interpretability tradeoff inherent in considering different numbers and sizes of communities, we analyze two publicly available networks-political weblogs and student friendships-and discuss how to interpret the network histogram when additional information related to node and edge labeling is present.