Modelling Trend Progression Through an Extension of the Polya Urn Process

M.C. ten Thij; Sandjai Bhulai

Modelling Trend Progression Through an Extension of the Polya Urn Process

M.C. ten Thij
, Sandjai Bhulai

Mathematics

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Knowing how and when trends are formed is a frequently visited research goal. In our work, we focus on the progression of trends through (social) networks. We use a random graph (RG) model to mimic the progression of a trend through the network. The context of the trend is not included in our model. We show that every state of the RG model maps to a state of the Polya process. We find that the limit of the component size distribution of the RG model shows power-law behaviour. These results are also supported by simulations.

Original language	English
Title of host publication	Advances in Network Science
Subtitle of host publication	12th International Conference and School, NetSci-X 2016, Wroclaw, Poland, January 11-13, 2016, Proceedings
Editors	A. Wierzbicki, U. Brandes, F. Schweitzer, D. Pedreschi
Publisher	Springer_Verlag
Pages	57-67
Volume	9564
Edition	1
ISBN (Print)	9783319283609
Publication status	Published - 13 Nov 2016
Event	12th International Conference and School of Network Science - Duration: 11 Jan 2016 → 13 Jan 2016

Conference

Conference	12th International Conference and School of Network Science
Abbreviated title	NetSci-X 2016
Period	11/01/16 → 13/01/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

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title = "Modelling Trend Progression Through an Extension of the Polya Urn Process",

abstract = "Knowing how and when trends are formed is a frequently visited research goal. In our work, we focus on the progression of trends through (social) networks. We use a random graph (RG) model to mimic the progression of a trend through the network. The context of the trend is not included in our model. We show that every state of the RG model maps to a state of the Polya process. We find that the limit of the component size distribution of the RG model shows power-law behaviour. These results are also supported by simulations.",

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ten Thij, MC & Bhulai, S 2016, Modelling Trend Progression Through an Extension of the Polya Urn Process. in A Wierzbicki, U Brandes, F Schweitzer & D Pedreschi (eds), Advances in Network Science: 12th International Conference and School, NetSci-X 2016, Wroclaw, Poland, January 11-13, 2016, Proceedings. 1 edn, vol. 9564, Springer_Verlag, pp. 57-67, 12th International Conference and School of Network Science, 11/01/16.

Modelling Trend Progression Through an Extension of the Polya Urn Process. / ten Thij, M.C.; Bhulai, Sandjai.
Advances in Network Science: 12th International Conference and School, NetSci-X 2016, Wroclaw, Poland, January 11-13, 2016, Proceedings. ed. / A. Wierzbicki; U. Brandes; F. Schweitzer; D. Pedreschi. Vol. 9564 1. ed. Springer_Verlag, 2016. p. 57-67.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Modelling Trend Progression Through an Extension of the Polya Urn Process

AU - ten Thij, M.C.

AU - Bhulai, Sandjai

PY - 2016/11/13

Y1 - 2016/11/13

N2 - Knowing how and when trends are formed is a frequently visited research goal. In our work, we focus on the progression of trends through (social) networks. We use a random graph (RG) model to mimic the progression of a trend through the network. The context of the trend is not included in our model. We show that every state of the RG model maps to a state of the Polya process. We find that the limit of the component size distribution of the RG model shows power-law behaviour. These results are also supported by simulations.

AB - Knowing how and when trends are formed is a frequently visited research goal. In our work, we focus on the progression of trends through (social) networks. We use a random graph (RG) model to mimic the progression of a trend through the network. The context of the trend is not included in our model. We show that every state of the RG model maps to a state of the Polya process. We find that the limit of the component size distribution of the RG model shows power-law behaviour. These results are also supported by simulations.

M3 - Conference contribution

SN - 9783319283609

VL - 9564

SP - 57

EP - 67

BT - Advances in Network Science

A2 - Wierzbicki, A.

A2 - Brandes, U.

A2 - Schweitzer, F.

A2 - Pedreschi, D.

PB - Springer_Verlag

T2 - 12th International Conference and School of Network Science

Y2 - 11 January 2016 through 13 January 2016

ER -

Modelling Trend Progression Through an Extension of the Polya Urn Process

Abstract

Conference

UN SDGs

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