The ground truth about metadata and community detection in networks

Peel, Leto; Larremore, Daniel B.; Clauset, Aaron

doi:10.1126/sciadv.1602548

Cited by 421 publications

(406 citation statements)

References 60 publications

(119 reference statements)

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“…Neuroscience aside, overlapping community structure should be investigated further. Recent mathematical results have indicated that the mapping of clusters to edge weights is many to one; the implication is that unless we know the true process by which our network was generated (and its relationship with its cluster structure), we cannot unambiguously claim that the detected clusters are "correct" [73]. This fact motivates the exploration of other approaches for grouping a network into clusters [18,74].…”

Section: Temporal Segregation Through Edge Clusteringmentioning

confidence: 99%

Temporal fluctuations in the brain’s modular architecture during movie-watching

Betzel

Byrge

Esfahlani

et al. 2019

Preprint

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Brain networks are flexible and reconfigure over time to support ongoing cognitive processes. However, tracking statistically meaningful reconfigurations across time has proven difficult. This has to do largely with issues related to sampling variability, making instantaneous estimation of network organization difficult, along with increased reliance on task-free (cognitively unconstrained) experimental paradigms, limiting the ability to interpret the origin of changes in network structure over time. Here, we address these challenges using time-varying network analysis in conjunction with a naturalistic viewing paradigm. Specifically, we developed a measure of inter-subject network similarity and used this measure as a coincidence filter to identify synchronous fluctuations in network organization across individuals. Applied to movie-watching data, we found that periods of high inter-subject similarity coincided with reductions in network modularity and increased connectivity between cognitive systems. In contrast, low inter-subject similarity was associated with increased system segregation and more rest-like architectures. We then used a data-driven approach to uncover clusters of functional connections that follow similar trajectories over time and are more strongly correlated during movie-watching than at rest. Finally, we show that synchronous fluctuations in network architecture over time can be linked to a subset of features in the movie. Our findings link dynamic fluctuations in network integration and segregation to patterns of intersubject similarity, and suggest that moment-to-moment fluctuations in FC reflect shared cognitive processing across individuals.

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Section: Temporal Segregation Through Edge Clusteringmentioning

confidence: 99%

Temporal fluctuations in the brain’s modular architecture during movie-watching

Betzel

Byrge

Esfahlani

et al. 2019

Preprint

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show abstract

“…The questions' persistent relevance is illustrated by a recent discussion of the tenuousness of the link between 'ground truths' about nodes and communities in networks. Between them, Hric et al (2014) and their critics (Peel et al 2016) came up with several reasons why topological community detection algorithms might not be able to reconstruct 'ground truths' about nodes. There might be more than one ground truth corresponding to the same network, these ground truths might be differently linked to the metadata describing network nodes, the network's community structure might not reflect the structure of the metadata representing a particular ground truths, or the community detection algorithm might perform poorly.…”

Section: Introductionmentioning

confidence: 99%

Same data—different results? Towards a comparative approach to the identification of thematic structures in science

2017

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Science studies are persistently challenged by the elusive structures of their subject matter, be it scientific knowledge or the various collectivities of researchers engaged with its production. Bibliometrics has responded by developing a strong and growing structural bibliometrics, which is concerned with delineating fields and identifying thematic structures. In the course of these developments, a concern emerged and is steadily growing. Do the sets of publications, authors or institutions we identify and visualise with our methods indeed represent thematic structures? To what extent are results of topic identification exercises determined by properties of knowledge structures, and to what extent are they determined by the approaches we use? Do we produce more than artefacts? These questions triggered the collective process of comparative topic identification reported in this special issue. The introduction traces the history of bibliometric approaches to topic identification, identifies the major challenges involved in these exercises, and introduces the contributions to the special issue.

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“…Despite the theoretical success of the CND in studying network structure, most realistic case studies of network systems have to consider both topology and node activities simultaneously. Examples are neural networks (Daido and Nakanishi 2004), power grids (Blaabjerg et al 2006), epidemic dynamics (Pastor-Satorras and Vespignani 2001), cascading effects in disaster spreading (Helbing 2013), individual fitness (Caldarelli et al 2002), social norms and collaborative expectations (Peyton Young 1998), co-evolutionary dynamics (Nardini et al 2008;Aoki and Aoyagi 2012), and data mining (Hric et al 2016;Peel et al 2017). However, in these studies the definitions of node activities and the methods to analyze them are highly problem-specific and have a dynamic nature.…”

Section: Introductionmentioning

confidence: 99%

Towards Quantitatively Understanding the Complexity of Social-Ecological Systems—From Connection to Consilience

Shi

Wang

et al. 2017

Int J Disaster Risk Sci

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The complexity of social-ecological systems (SES) is rooted in the outcomes of node activities connected by network topology. Thus far, in network dynamics research, the connectivity degree (CND), indicating how many nodes are connected to a given node, has been the dominant concept. However, connectivity focuses only on network topology, neglecting the crucial relation to node activities, and thereby leaving system outcomes largely unexplained. Inspired by the phenomenon of ''consensus of wills and coordination of activities'' often observed in disaster risk management, we propose a new concept of network characteristic, the consilience degree (CSD), aiming to measure the way in which network topology and node activities together contribute to system outcomes. The CSD captures the fact that nodes may assume different states that make their activities more or less compatible. Connecting two nodes with in/compatible states will lead to outcomes that are un/desirable from the perspective of the SES in question. We mathematically prove that the CSD is a generalized CND, and the CND is a special case of CSD. As a general, fundamental concept, the CSD can facilitate the development of a new framework of network properties, models, and theories that allows us to understand patterns of network behavior that cannot be explained in terms of connectivity alone. We further demonstrate that a co-evolutionary mechanism can naturally improve the CSD. Given the generality of co-evolution in SES, we argue that the CSD is an inherent attribute rather than an artificial concept, which underpins the fundamental importance of the CSD to the study of SES.

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The ground truth about metadata and community detection in networks

Abstract: Troubles with community detection in networks: No ground truth, no free lunch, and the complex coupling of metadata with structure.

Cited by 421 publications

References 60 publications

Temporal fluctuations in the brain’s modular architecture during movie-watching

Temporal fluctuations in the brain’s modular architecture during movie-watching

Same data—different results? Towards a comparative approach to the identification of thematic structures in science

Towards Quantitatively Understanding the Complexity of Social-Ecological Systems—From Connection to Consilience

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