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Journal articlePalmieri L, Jensen HJ, 2020,
The forest fire model: the subtleties of criticality and scale invariance
, Frontiers in Physics, Vol: 8, Pages: 1-8, ISSN: 2296-424XAmongst the numerous models introduced with SOC, the Forest Fire Model (FFM) is particularly attractive for its close relationship to stochastic spreading, which is central to the study of systems as diverse as epidemics, rumors, or indeed, fires. However, since its introduction, the nature of the model's scale invariance has been controversial, and the lack of scaling observed in many studies diminished its theoretical attractiveness. In this study, we analyse the behavior of the tree density, the average cluster size and the largest cluster and show that the model could be of high practical relevance for the activation dynamics seen in brain and rain studies. From this perspective, its peculiar scaling properties should be regarded as an asset rather than a limitation.
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Journal articleChen J, Wang Z, Zhu T, et al., 2020,
Recommendation algorithm in double-layer network based on vector dynamic evolution clustering and attention mechanism
, Complexity, Vol: 2020, Pages: 1-19, ISSN: 1076-2787The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.
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Conference paperRosas De Andraca FE, Azari M, Arani A, 2020,
Mobile Cellular-Connected UAVs: Reinforcement Learning for Sky Limits
, IEEE Globecom Workshops 2020 -
Journal articleEvans TS, Calmon L, Vasiliauskaite V, 2020,
Longest path in the price model
, Scientific Reports, Vol: 10, Pages: 1-9, ISSN: 2045-2322The Price model, the directed version of the Barab\'{a}si-Albert model,produces a growing directed acyclic graph. We look at variants of the model inwhich directed edges are added to the new vertex in one of two ways: usingcumulative advantage (preferential attachment) choosing vertices in proportionto their degree, or with random attachment in which vertices are chosenuniformly at random. In such networks, the longest path is well defined and insome cases is known to be a better approximation to geodesics than the shortestpath. We define a reverse greedy path and show both analytically andnumerically that this scales with the logarithm of the size of the network witha coefficient given by the number of edges added using random attachment. Thisis a lower bound on the length of the longest path to any given vertex and weshow numerically that the longest path also scales with the logarithm of thesize of the network but with a larger coefficient that has some weak dependenceon the parameters of the model.
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Journal articleFalkenberg M, Lee J-H, Amano S-I, et al., 2020,
Identifying time dependence in network growth
, Physical Review & Research International, Vol: 2, Pages: 023352 – 1-023352 – 17, ISSN: 2231-1815Identifying power-law scaling in real networks—indicative of preferential attachment—has proved controversial. Critics argue that measuring the temporal evolution of a network directly is better than measuring the degree distribution when looking for preferential attachment. However, many of the established methods do not account for any potential time dependence in the attachment kernels of growing networks, or methods assume that node degree is the key observable determining network evolution. In this paper, we argue that these assumptions may lead to misleading conclusions about the evolution of growing networks. We illustrate this by introducing a simple adaptation of the Barabási-Albert model, the “k2 model,” where new nodes attach to nodes in the existing network in proportion to the number of nodes one or two steps from the target node. The k2 model results in time dependent degree distributions and attachment kernels, despite initially appearing to grow as linear preferential attachment, and without the need to include explicit time dependence in key network parameters (such as the average out-degree). We show that similar effects are seen in several real world networks where constant network growth rules do not describe their evolution. This implies that measurements of specific degree distributions in real networks are likely to change over time.
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Journal articleCiacci A, Falkenberg M, Manani KA, et al., 2020,
Understanding the transition from paroxysmal to persistent atrial fibrillation
, Physical Review Research, Vol: 2, Pages: 023311-023311Atrial fibrillation (AF) is the most common cardiac arrhytmia, characterisedby the chaotic motion of electrical wavefronts in the atria. In clinicalpractice, AF is classified under two primary categories: paroxysmal AF, shortintermittent episodes separated by periods of normal electrical activity, andpersistent AF, longer uninterrupted episodes of chaotic electrical activity.However, the precise reasons why AF in a given patient is paroxysmal orpersistent is poorly understood. Recently, we have introduced the percolationbased Christensen-Manani-Peters (CMP) model of AF which naturally exhibits bothparoxysmal and persistent AF, but precisely how these differences emerge in themodel is unclear. In this paper, we dissect the CMP model to identify the causeof these different AF classifications. Starting from a mean-field model wherewe describe AF as a simple birth-death process, we add layers of complexity tothe model and show that persistent AF arises from the formation of temporallystable structural re-entrant circuits that form from the interaction ofwavefront collisions during paroxysmal AF. These results are compatible withrecent findings suggesting that the formation of re-entrant drivers in fibroticborder zones perpetuates persistent AF.
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Journal articleJensen H, 2020,
Universality classes and information-theoretic measures of complexity via group entropies
, Scientific Reports, Vol: 10, Pages: 1-11, ISSN: 2045-2322We introduce a class of information measures based on group entropies, allowing us to describe the information-theoreticalproperties of complex systems. These entropic measures are nonadditive, and are mathematically deduced from a seriesof natural axioms. In addition, we require extensivity in order to ensure that our information measures are meaningful. Theinformation measures proposed are suitably defined for describing universality classes of complex systems, each characterizedby a specific state space growth rate function.
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Journal articlePalmieri L, Jensen HJ, 2020,
Investigating critical systems via the distribution of correlation lengths
, PHYSICAL REVIEW RESEARCH, Vol: 2- Author Web Link
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Journal articleVasiliauskaite V, Evans TS, 2020,
Making communities show respect for order
, Applied Network Science, Vol: 5, Pages: 1-24, ISSN: 2364-8228In this work we give a community detection algorithm in which the communities both respects the intrinsic order of a directed acyclic graph and also finds similar nodes. We take inspiration from classic similarity measures of bibliometrics, used to assess how similar two publications are, based on their relative citation patterns. We study the algorithm’s performance and antichain properties in artificial models and in real networks, such as citation graphs and food webs. We show how well this partitioning algorithm distinguishes and groups together nodes of the same origin (in a citation network, the origin is a topic or a research field). We make the comparison between our partitioning algorithm and standard hierarchical layering tools as well as community detection methods. We show that our algorithm produces different communities from standard layering algorithms.
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Journal articleViegas E, Goto H, Kobayashi Y, et al., 2020,
Allometric scaling of mutual information in complex networks: a conceptual framework and empirical approach
, Entropy: international and interdisciplinary journal of entropy and information studies, Vol: 22, Pages: 1-14, ISSN: 1099-4300Complexity and information theory are two very valuable but distinct fields of research, yet sharing the same roots. Here, we develop a complexity framework inspired by the allometric scaling laws of living biological systems in order to evaluate the structural features of networks. This is done by aligning the fundamental building blocks of information theory (entropy and mutual information) with the core concepts in network science such as the preferential attachment and degree correlations. In doing so, we are able to articulate the meaning and significance of mutual information as a comparative analysis tool for network activity. When adapting and applying the framework to the specific context of the business ecosystem of Japanese firms, we are able to highlight the key structural differences and efficiency levels of the economic activities within each prefecture in Japan. Moreover, we propose a method to quantify the distance of an economic system to its efficient free market configuration by distinguishing and quantifying two particular types of mutual information, total and structural.
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