The shape of collaborations

Patania, Alice; Petri, Giovanni; Vaccarino, Francesco

doi:10.1140/epjds/s13688-017-0114-8

Cited by 197 publications

(128 citation statements)

References 36 publications

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“…This contrasts from typical network analyses that study a single snapshot of a social network. Previously, researchers have circumvented this issue by looking at, for example, sets of disparate subgraphs from a larger graph (Traud, Mucha, and Porter 2012;Patania, Petri, and Vaccarino 2017); samples of ego networks (Ugander, Backstrom, and Kleinberg 2013;Mcauley and Leskovec 2014); and collections of snapshots of time-evolving networks (Yaveroglu et al 2014).…”

Section: Discussionmentioning

confidence: 99%

Modelling and analysis of tagging networks in Stack Exchange communities

Benson

2019

Journal of Complex Networks

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Large Question-and-Answer (Q&A) platforms support diverse knowledge curation on the Web. While researchers have studied user behavior on the platforms in a variety of contexts, there is relatively little insight into important by-products of user behavior that also encode knowledge. Here, we analyze and model the macroscopic structure of tags applied by users to annotate and catalog questions, using a collection of 168 Stack Exchange websites. We find striking similarity in tagging structure across these Stack Exchange communities, even though each community evolves independently (albeit under similar guidelines). Using our empirical findings, we develop a simple generative model that creates random bipartite graphs of tags and questions. Our model accounts for the tag frequency distribution but does not explicitly account for co-tagging correlations. Even under these constraints, we demonstrate empirically and theoretically that our model can reproduce a number of statistical properties of the co-tagging graph that links tags appearing in the same post.

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Section: Discussionmentioning

confidence: 99%

Modelling and analysis of tagging networks in Stack Exchange communities

Benson

2019

Journal of Complex Networks

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“…In social systems they can for instance be suited to describe collaboration networks, where nodes denote authors and hyperedges stand for groups of authors, who have written papers together. Alternatively, hypergraphs can be invoked to describe face-to-face social networks where individuals can interact in groups of arbitrary sizes [26]. In biology, hypergraphs allow to properly model biochemical reactions simultaneously involving more than two species, or conveniently describe higher-order interactions among different families of proteins [15].…”

Section: Introductionmentioning

confidence: 99%

Random walks on hypergraphs

Battiston²,

et al. 2020

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In the last twenty years network science has proven its strength in modelling many real-world interacting systems as generic agents, the nodes, connected by pairwise edges. Yet, in many relevant cases, interactions are not pairwise but involve larger sets of nodes, at a time. These systems are thus better described in the framework of hypergraphs, whose hyperedges effectively account for multibody interactions. We hereby propose a new class of random walks defined on such higher-order structures, and grounded on a microscopic physical model where multi-body proximity is associated to highly probable exchanges among agents belonging to the same hyperedge. We provide an analytical characterisation of the process, deriving a general solution for the stationary distribution of the walkers. The dynamics is ultimately driven by a generalised random walk Laplace operator that reduces to the standard random walk Laplacian when all the hyperedges have size 2 and are thus meant to describe pairwise couplings. We illustrate our results on synthetic models for which we have a full control of the high-order structures, and real-world networks where higher-order interactions are at play. As a first application of the method, we compare the behaviour of random walkers on hypergraphs to that of traditional random walkers on the corresponding projected networks, drawing interesting conclusions on node rankings in collaboration networks. As a second application, we show how information derived from the random walk on hypergraphs can be successfully used for classification tasks involving objects with several features, each one represented by a hyperedge. Taken together, our work contributes to unveiling the effect of higher-order interactions on diffusive processes in higher-order networks, shading light on mechanisms at the hearth of biased information spreading in complex networked systems.

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“…Among those, multi-body models, sometimes called combinatorial models, focus on the importance of group interactions, that is situations when the basic unit of interaction involves more than two nodes. Multi-body interactions are observed in different areas in nature [2], society [3] and technology [4], with examples ranging from collaborations of authors [5] to neuronal activity [6,7]. Such systems may be represented as hypergraphs or simplicial complexes, and a substantial body of work has characterised their structural properties.…”

Section: Introductionmentioning

confidence: 99%

Multibody interactions and nonlinear consensus dynamics on networked systems

2020

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Multi-body interactions can reveal higher-order dynamical effects that are not captured by traditional two-body network models. In this work, we derive and analyse models for consensus dynamics on hypergraphs, where nodes interact in groups rather than in pairs. Our work reveals that multi-body dynamical effects that go beyond rescaled pairwise interactions can only appear if the interaction function is non-linear, regardless of the underlying multi-body structure. As a practical application, we introduce a specific non-linear function to model three-body consensus, which incorporates reinforcing group effects such as peer pressure. Unlike consensus processes on networks, we find that the resulting dynamics can cause shifts away from the average system state. The nature of these shifts depends on a complex interplay between the distribution of the initial states, the underlying structure and the form of the interaction function. By considering modular hypergraphs, we discover state-dependent, asymmetric dynamics between polarised clusters where multi-body interactions make one cluster dominate the other.

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The shape of collaborations

Cited by 197 publications

References 36 publications

Modelling and analysis of tagging networks in Stack Exchange communities

Modelling and analysis of tagging networks in Stack Exchange communities

Random walks on hypergraphs

Multibody interactions and nonlinear consensus dynamics on networked systems

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