Naming Game on Adaptive Weighted Networks

Lipowska, Dorota; Lipowski, Adam

doi:10.1162/artl_a_00067

Cited by 25 publications

(18 citation statements)

References 46 publications

(47 reference statements)

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“…Fig 1 shows the evolution of N d , N w and S over time for a LFR network with 1000 agents, μ = 0.3, 〈 k 〉 = 20, τ 1 = 2, τ 2 = 1, k max = 50, size of communities C min = 10, C max = 50. We adopted the unit T = N communication attempts as defined in [ 8 ]. All simulations in this work were executed and averaged over 100 games.…”

Section: Methodsmentioning

confidence: 99%

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Detection of communities with Naming Game-based methods

Uzun

Ribeiro

2017

PLoS ONE

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Complex networks are often organized in groups or communities of agents that share the same features and/or functions, and this structural organization is built naturally with the formation of the system. In social networks, we argue that the dynamic of linguistic interactions of agreement among people can be a crucial factor in generating this community structure, given that sharing opinions with another person bounds them together, and disagreeing constantly would probably weaken the relationship. We present here a computational model of opinion exchange that uncovers the community structure of a network. Our aim is not to present a new community detection method proper, but to show how a model of social communication dynamics can reveal the (simple and overlapping) community structure in an emergent way. Our model is based on a standard Naming Game, but takes into consideration three social features: trust, uncertainty and opinion preference, that are built over time as agents communicate among themselves. We show that the separate addition of each social feature in the Naming Game results in gradual improvements with respect to community detection. In addition, the resulting uncertainty and trust values classify nodes and edges according to role and position in the network. Also, our model has shown a degree of accuracy both for non-overlapping and overlapping communities that are comparable with most algorithms specifically designed for topological community detection.

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Section: Methodsmentioning

confidence: 99%

“…Trust will be used, then, as a social feature that influences the language interactions. It will be modeled as the weight of the edge that links both individuals (similarly to the definition of trust in [ 7 ]), in a variation of the NG called Naming Game with Adaptive Weights (NG-AW) [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of communities with Naming Game-based methods

Uzun

Ribeiro

2017

PLoS ONE

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“…Można też oczekiwać, że to nie każdy z parametrów i z osobna kontroluje system, lecz raczej jakaś ich kombinacja. Choć brak jeszcze ścisłego na to dowodu, w pracy (Lipowska, 2011a) przedstawione są pewne argumenty na rzecz tego, że rolę parametru kontrolnego modelu pełni iloczyn .…”

Section: Definicja Modeluunclassified

Gra W Nazywanie Z Preferencyjnym Wyborem Partnerów

Lipowska

2012

10.14746/il

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In this paper, a naming game on an adaptive weighted network is examined. A weight of connection for a given pair of agents depends on their communication success rate and determines the probability with which the agents communicate. When the preference toward successfully communicating agents is not so strong, the model behaves similarly to the naming game on a complete graph. In particular, it quickly reaches a single-language state, albeit some details of the dynamics are different from the complete-graph version. Much different behaviour appears when the preference toward successfully communicating agents is stronger and the model gets trapped in a multi-language regime. In this case gradual coarsening and extinction of languages lead to the emergence of a dominant language, albeit with some other languages still being present. A comparison of distribution of languages in the model and in the human population is discussed.

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“…This approach simulates agents simultaneously playing two roles, both speaker and hearer. In real life, however, knowledge-sharing for speaking with meaning and listening with understanding also comes from social gatherings [10,11], i.e. cultural knowledge, so individuals are able to adjust their beliefs like a form of social introspection.…”

Section: B Introductionmentioning

confidence: 99%

Using Cultural and Social Beliefs in Language Games

Limsatta

Sornil

2016

J. ICT Res. Appl.

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Abstract. Agreement on word-object pairing in communication depends on the intensity of the beliefs that gradually emerge in a society of agents, on the condition that no one was born with embedded knowledge. The agents search and exchange ideas about unknown word-object pairings, until they meet a consensus about what the object should be named. A language game is a social process of finding agreement on word-object pairings through communication in a multi-agent system. In this paper, a technique is proposed to discover the association between a word and the agents' beliefs on an object using selforganizing maps and a cultural algorithm in a multi-hearer environment. A conceptual space is implemented, which stores the agent's beliefs in three dimensions, represented by colors. The technique was evaluated for a variety of scenarios using four significant measures: coherence, specificity, success rate, and word size. The results showed that with the proposed method social agents can reach agreement fast and that their communication is effective.

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Naming Game on Adaptive Weighted Networks

Cited by 25 publications

References 46 publications

Detection of communities with Naming Game-based methods

Detection of communities with Naming Game-based methods

Gra W Nazywanie Z Preferencyjnym Wyborem Partnerów

Using Cultural and Social Beliefs in Language Games

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