Nestedness in complex networks: Observation, emergence, and implications

Mariani, Manuel Sebastian; Ren, Zhuo-Ming; Bascompte, Jordi; Tessone, Claudio J.

doi:10.1016/j.physrep.2019.04.001

Cited by 184 publications

(147 citation statements)

References 338 publications

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“…From a statistical physics perspective, one can formulate a growing network model where, at each step, a selected agent (with probability α) creates a connection to the most central agent they are not connected to, or (with probability 1 − α) deletes its connection to the least-central agent among its neighbors. The model features a phase transition at a critical value α = α c which separates a phase where the resulting network is fully-connected (α > α c ) from a phase where the network exhibits a highly centralized topology (α < α c ), namely a perfectly nested one where agents' interaction are hierarchically arranged [65]. Importantly, this result holds regardless of the algorithm employed by the agents to assess the centrality of the other agents [63].…”

Section: Systemic Consequencesmentioning

confidence: 97%

“…First, in a social network, when agents strive to connect to high-ranked agents (i.e., central agents) and delete their links to low-ranked agents (i.e., peripheral agents), highly-hierarchical societies emerge, with reduced social mobility [59,[63][64][65]. From a statistical physics perspective, one can formulate a growing network model where, at each step, a selected agent (with probability α) creates a connection to the most central agent they are not connected to, or (with probability 1 − α) deletes its connection to the least-central agent among its neighbors.…”

Section: Systemic Consequencesmentioning

confidence: 99%

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Network-based ranking in social systems: three challenges

Lü¹

2020

J. Phys. Complex.

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Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (1) rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.

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Section: Systemic Consequencesmentioning

confidence: 97%

Section: Systemic Consequencesmentioning

confidence: 99%

Network-based ranking in social systems: three challenges

Lü¹

2020

J. Phys. Complex.

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“…Unfortunately, this situation is quite common for mutualist ecosystems which are in general very sparse and often eccentric, with typically much more animal species than plant species, leading to a non negligible degree degeneracy. For this reason, a variant of this metrics called stable-NODF has recently been proposed by Mariani et al [24], which does not incorporate the decreasing fill term and hence does not penalize the degree repetition. Thus, this metrics measures solely the number of shared partners among pairs of rows and columns.…”

Section: The Studied Metricsmentioning

confidence: 99%

“…Despite its popularity, this metrics was already known to have several flaws [25] and various authors have outlined the presence of ambiguous steps in its calculation [13,24]. Indeed, Almeida-Neto et , summarizes the results of the multi-linear fit detailed in Eq.…”

Section: Temperaturementioning

confidence: 99%

Measuring Nestedness: A comparative study of the performance of different metrics

Payrató-Borràs

Hernández

Moreno

2020

Preprint

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1. Nestedness is a property of interaction networks widely observed in natural mutualistic communities, like plant-pollinators or plant-seed dispersers, among other systems. A perfectly nested network is characterized by the peculiarity that the interactions of any node form a subset of the interactions of all nodes with higher degree. Despite a widespread interest on this pattern, no general consensus exists on how to measure it. Instead, several metrics aiming at quantifying nestedness, based on different but not necessarily independent properties of the networks, coexist in the literature blurring the comparison between ecosystems.2. In this work we present a detailed critical study of the behavior of six popular nestedness metrics and the variants of two of them. In order to evaluate their performance, we compare the obtained values of the nestedness of a large set of real networks among them and against a maximum entropy and maximum likelihood null model. We also analyze the dependencies of each metrics on different network parameters as size, fill and eccentricity.3. Our results point out, first, that the metrics do not rank the degree of nestedness of networks universally. Furthermore, several metrics show significant undesired dependencies on the network properties considered. The study of these dependencies allows us to understand some of the systematic shifts between the real values of nestedness and the average over the null model. 4. This paper intends to provide readers with a critical guide on how to measure nestedness patterns, by explaining the functioning of six standard metrics and two of its variants, and then disclosing its qualities and flaws. By doing so, we also aim to extend the application of the recently proposed null models based on maximum entropy to the still largely unexplored area of ecological networks.5. Finally, to complement the guide, we provide a fully-documented repository named nullnest which gathers the codes to produce the null model and calculate the nestedness index -both the real value and the null expectation-using the studied metrics. The repository contains, moreover, the main results of the null model applied to a large dataset of more than 200 bipartite networks.

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“…This article focuses on one of the fundamental problems in network science, the detection of communities [18], which has received enormous attention from diverse research areas, including physics [18], computer science [19], ecology [20], neuroscience [21], and social science [6,22], among others. While the problem is not uniquely defined [18], it can be generically described as the problem of determining whether there exists a meaningful partition of the network nodes into groups of nodes.…”

Section: Introductionmentioning

confidence: 99%

Optimal timescale for community detection in growing networks

2019

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Time-stamped data are increasingly available for many social, economic, and information systems that can be represented as networks growing with time. The World Wide Web, social contact networks, and citation networks of scientific papers and online news articles, for example, are of this kind. Static methods can be inadequate for the analysis of growing networks as they miss essential information on the system's dynamics. At the same time, time-aware methods require the choice of an observation timescale, yet we lack principled ways to determine it. We focus on the popular community detection problem which aims to partition a network's nodes into meaningful groups. We use a multi-layer quality function to show, on both synthetic and real datasets, that the observation timescale that leads to optimal communities is tightly related to the system's intrinsic aging timescale that can be inferred from the time-stamped network data. The use of temporal information leads to drastically different conclusions on the community structure of real information networks, which challenges the current understanding of the large-scale organization of growing networks. Our findings indicate that before attempting to assess structural patterns of evolving networks, it is vital to uncover the timescales of the dynamical processes that generated them.A popular approach to community detection is to maximize a function, called modularity, which quantifies how much the total number of intra-community edges deviates from its expected value under a null model that preserves the individual nodes' number of connections [24]. Modularity has been studied from many viewpoints, and it is widely-recognized as a standard tool in network analysis [18]. Despite past research and the wide use of modularity optimization in a broad range of contexts, we still lack a systematic understanding of its behavior and performance in growing networks where time and aging phenomena are fundamental [25][26][27]. Albeit modularity has been used in such systems in its original form [28][29][30], the results can be expected to be suboptimal as modularity neglects the vital time information. A multi-layer form of modularity has been developed that can take into account network snapshots at various times [31,32]. However, when we wish to apply a multi-layer approach to identify relevant communities in growing networks, we face an impasse: existing works assume layered input data [31][32][33][34] and thus they do not consider the question of how to divide an arbitrary time-stamped network into layers. Addressing this question requires to choose an appropriate observation timescale, i.e. the temporal duration for each layer [5,35,36]. This choice is essential because different timescales might reveal substantially different community structures, which in turn might lead to different conclusions on the large-scale organization of the system.In this work, we derive analytically a criterion to estimate when a time-aggregated, static view of a growing network ceases to be ...

show abstract

Nestedness in complex networks: Observation, emergence, and implications

Cited by 184 publications

References 338 publications

Network-based ranking in social systems: three challenges

Network-based ranking in social systems: three challenges

Measuring Nestedness: A comparative study of the performance of different metrics

Optimal timescale for community detection in growing networks

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