The ML-Model for Multi-layer Social Networks

Magnani, Matteo; Rossi, Luca

doi:10.1109/asonam.2011.114

Cited by 134 publications

(97 citation statements)

References 8 publications

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“…Links posted by the user, and the content of their user profile, also provide additional linguistic context. Thirdly, methods may also bring the author's friends content as additional context, possibly even linking to other sites, using a multi-layered social network model [58].…”

Section: Resultsmentioning

confidence: 99%

Analysis of named entity recognition and linking for tweets

Derczynski

Maynard

Rizzo

et al. 2015

Information Processing & Management

276

186

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Applying natural language processing for mining and intelligent information access to tweets (a form of microblog) is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, context-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new Twitter entity disambiguation dataset, and conduct an empirical analysis of named entity recognition and disambiguation, investigating how robust a number of state-of-the-art systems are on such noisy texts, what the main sources of error are, and which problems should be further investigated to improve the state of the art.

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Section: Resultsmentioning

confidence: 99%

Analysis of named entity recognition and linking for tweets

Derczynski

Maynard

Rizzo

et al. 2015

Information Processing & Management

276

186

View full text Add to dashboard Cite

show abstract

“…The general approach to model a network is not general enough to ascertain the true interdependence that arises in some complex systems. Examples of such a systems are: user relationships in different social networks [1], transportation systems [2], or the learning organization in the brain [3]. Particularly interesting are those topologies of interconnected networks called multiplex, see Fig.…”

Section: Introductionmentioning

confidence: 99%

Spectral properties of the Laplacian of multiplex networks

et al. 2013

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One of the more challenging tasks in the understanding of dynamical properties of models on top of complex networks is to capture the precise role of multiplex topologies. In a recent paper, Gómez et al. [Phys. Rev. Lett. 110, 028701 (2013)], some of the authors proposed a framework for the study of diffusion processes in such networks. Here, we extend the previous framework to deal with general configurations in several layers of networks and analyze the behavior of the spectrum of the Laplacian of the full multiplex. We derive an interesting decoupling of the problem that allow us to unravel the role played by the interconnections of the multiplex in the dynamical processes on top of them. Capitalizing on this decoupling we perform an asymptotic analysis that allow us to derive analytical expressions for the full spectrum of eigenvalues. This spectrum is used to gain insight into physical phenomena on top of multiplex, specifically, diffusion processes and synchronizability.

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“…Multidimensional approaches like the work of Brodka et al [14] where they define a multi-layer social network as a set of single-layered social graphs with fixed set of nodes and variation on their edges, the work of M. Magnani and L.Rossi [15] where was proposed a multidimensional model where each layer corresponded to an OSN and a node mapping function between those layers, the work of A. Socievole et al [13] where was presented a model with a routing protocol that makes use of multi-layer social networks to select nodes in order to act as message relays, or even the work of Forestier et al [16] where was proposed a multidimensional social network from online discussions with relations derived from structure and text content.…”

Section: Multidimensional Networkmentioning

confidence: 99%

Multidimensional Approach to Online Interest Networks

Barbosa¹,

Santos²

2017

dtcse

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Abstract. Online social networks allow users to socialise with others and also construct and manage their identities via self-presentation. This creates a network of interests that is often represented by a single network that aggregates all of the connections, however it is possible to increase the possibilities by dividing it into layers of individual networks. In this paper we propose a model for a multidimensional network of interests based on the interests that online social network users provide on their profiles. This multidimensional approach can be useful for getting insights about user behaviour or even help organisations understand the impact of their online content.

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The ML-Model for Multi-layer Social Networks

Cited by 134 publications

References 8 publications

Analysis of named entity recognition and linking for tweets

Analysis of named entity recognition and linking for tweets

Spectral properties of the Laplacian of multiplex networks

Multidimensional Approach to Online Interest Networks

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