Vasiliauskaite, VaivaEvans, Tim S.Expert, Paul
22页查看更多>>摘要：In this paper, we employ the decomposition of a directed network as an undirected graph plus its associated node meta-data to characterise the cyclic structure found in directed networks by finding a Minimal Cycle Basis of the undirected graph and augmenting its components with direction information. We show that only four classes of directed cycles exist, and that they can be fully distinguished by the organisation and number of source-sink node pairs and their antichain structure. We are particularly interested in Directed Acyclic Graphs and introduce a set of metrics that characterise the Minimal Cycle Basis using the Directed Acyclic Graphs meta-data information. In particular, we numerically show that transitive reduction stabilises the properties of Minimal Cycle Bases measured by the metrics we introduced while retaining key properties of the Directed Acyclic Graph. This makes the metrics a consistent characterisation of Directed Acyclic Graphs and the systems they represent. We measure the characteristics of the Minimal Cycle Bases of four models of transitively reduced Directed Acyclic Graphs and show that the metrics introduced are able to distinguish the models and are sensitive to their generating mechanisms. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC
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NSTL
Elsevier
Hernandez, G.Martin del Rey, A.
13页查看更多>>摘要：A framework that allows the incorporation of community structure into epidemiological compartmental models has been developed. The models resulting from this process are compartmental models as well, which are related to the base models. This work includes an existence and uniqueness theorem, showing that, under certain conditions on the mobility, epidemiological models in which f(t, X) is continuous in time and Lipschitz continuous on the compartments induce unique community models; and a homogeneous mixing limit, showing that under high mobility conditions the base model is recovered in the global population. Applications of the SIR model and the impact of the community structure on the estimation of their effective parameters are discussed in detail. An open computational implementation of this framework is available to the scientific community. It allows modeling community distribution using mobility data, as shown with Spain data during the 2020 state of alarm. (C) 2022 The Author(s). Published by Elsevier B.V.
原文链接:- NSTL
- Elsevier
Qiu, TianLu, TianChen, GuangZhang, Zi-Ke...
11页查看更多>>摘要：As an effective tool of information filtering, the network-based recommendation algorithms encounter the challenging problem of recommendation bias induced by the object heterogeneity. Previous solutions usually make the improvement based on some specific algorithm, however, are difficult to generalize to different algorithms. In this article, we propose an improved model with a general formula, by inhibiting recommendation bias described by the eigenvalue and eigenvectors of the algorithm similarity matrix, and applied the model into ten different algorithms. Based on four real recommender systems, the experimental results show that nearly all the algorithms are improved in three aspects of recommendation accuracy, diversity and novelty, for all the four datasets. The recommendation accuracy of cold objects is also elevated. Especially, two excellent algorithms are further improved without introducing any other parameter. Our work may shed a new light on developing general recommendation algorithms from the perspective of revealing intrinsic feature in recommender systems. (C) 2022 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
