Community event prediction in dynamic social networks

Nagehan Ilhan; Şule Gündüz Ögüdücü

doi:10.1109/ICMLA.2013.40

Community event prediction in dynamic social networks

Nagehan Ilhan, Şule Gündüz Ögüdücü

Department of Artificial Intelligence and Data Engineering

Istanbul Technical University

Research output: Contribution to conference › Paper › peer-review

10 Citations (Scopus)

Abstract

Communities are fundamental units of every social network, their structure and evolution are essential to understanding the structure and functionality of large networks. Also, community evolution prediction is an important task with various real-life applications in social network analysis. In this paper, we present a framework for modeling community evolution prediction in social networks. Each community is characterized by a wide range of structural features to describe community characteristics and a series of evolutionary events. A community matching algorithm is also proposed to efficiently identify and track similar communities over time. Experiments on different data sets prove that a high rate of community evolution prediction has been achieved.

Original language	English
Pages	191-196
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLA.2013.40
Publication status	Published - 2013
Event	2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 - Miami, FL, United States Duration: 4 Dec 2013 → 7 Dec 2013

Conference

Conference	2013 12th International Conference on Machine Learning and Applications, ICMLA 2013
Country/Territory	United States
City	Miami, FL
Period	4/12/13 → 7/12/13

Keywords

Community Evolution
Predicting Community Evolution
Social Network

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10.1109/ICMLA.2013.40

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abstract = "Communities are fundamental units of every social network, their structure and evolution are essential to understanding the structure and functionality of large networks. Also, community evolution prediction is an important task with various real-life applications in social network analysis. In this paper, we present a framework for modeling community evolution prediction in social networks. Each community is characterized by a wide range of structural features to describe community characteristics and a series of evolutionary events. A community matching algorithm is also proposed to efficiently identify and track similar communities over time. Experiments on different data sets prove that a high rate of community evolution prediction has been achieved.",

keywords = "Community Evolution, Predicting Community Evolution, Social Network",

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year = "2013",

doi = "10.1109/ICMLA.2013.40",

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note = "2013 12th International Conference on Machine Learning and Applications, ICMLA 2013 ; Conference date: 04-12-2013 Through 07-12-2013",

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AU - Ilhan, Nagehan

AU - Ögüdücü, Şule Gündüz

PY - 2013

Y1 - 2013

N2 - Communities are fundamental units of every social network, their structure and evolution are essential to understanding the structure and functionality of large networks. Also, community evolution prediction is an important task with various real-life applications in social network analysis. In this paper, we present a framework for modeling community evolution prediction in social networks. Each community is characterized by a wide range of structural features to describe community characteristics and a series of evolutionary events. A community matching algorithm is also proposed to efficiently identify and track similar communities over time. Experiments on different data sets prove that a high rate of community evolution prediction has been achieved.

AB - Communities are fundamental units of every social network, their structure and evolution are essential to understanding the structure and functionality of large networks. Also, community evolution prediction is an important task with various real-life applications in social network analysis. In this paper, we present a framework for modeling community evolution prediction in social networks. Each community is characterized by a wide range of structural features to describe community characteristics and a series of evolutionary events. A community matching algorithm is also proposed to efficiently identify and track similar communities over time. Experiments on different data sets prove that a high rate of community evolution prediction has been achieved.

KW - Community Evolution

KW - Predicting Community Evolution

KW - Social Network

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U2 - 10.1109/ICMLA.2013.40

DO - 10.1109/ICMLA.2013.40

M3 - Paper

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EP - 196

T2 - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013

Y2 - 4 December 2013 through 7 December 2013

ER -

Community event prediction in dynamic social networks

Abstract

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Keywords

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