Theodore Papageorgiou - Academia.edu (original) (raw)
Papers by Theodore Papageorgiou
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016
The study of a large real world network in terms of graph sample representation constitutes a ver... more The study of a large real world network in terms of graph sample representation constitutes a very powerful and useful tool in several domains of network analysis. This is the motivation that has led the work of this paper towards the development of a new graph sampling algorithm. Previous research in this area proposed simple processes such as the classic Random Walk algorithm, Random node and Random edge sampling and has evolved during the last decade to more advanced graph exploration approaches such as Forest Fire and Frontier sampling. In this paper, we propose a new graph sampling method based on edge selection. In addition, we crawled Facebook collecting a large dataset consisting of 10 million users and 80 million users' relations, which we have also used to evaluate our sampling algorithm. The experimental evaluation on several datasets proves that our approach preserves several properties of the initial graphs, leading to representative samples and outperforms all the other approaches.
The study of a large real world network in terms of graph sample representation constitutes a ver... more The study of a large real world network in terms
of graph sample representation constitutes a very powerful and
useful tool in several domains of network analysis. This is
the motivation that has led the work of this paper towards
the development of a new graph sampling algorithm. Previous
research in this area proposed simple processes such as the
classic Random Walk algorithm, Random node and Random
edge sampling and has evolved during the last decade to more
advanced graph exploration approaches such as Forest Fire
and Frontier sampling. In this paper, we propose a new graph
sampling method based on edge selection. In addition, we crawled
Facebook collecting a large dataset consisting of 10 million users and 80 million users’ relations, which we have also used to evaluate our sampling algorithm. The experimental evaluation on several datasets proves that our approach preserves several properties of the initial graphs, leading to representative samples and outperforms all the other approaches.
In 2012 IEEE International Conference on Green Computing and Communications, GreenCom 2012, Besancon, France, November 20-23, 2012
We investigate the correlation between the social network communities as defined by a community d... more We investigate the correlation between the social network communities as defined by a community detection algorithm and the Facebook pages annotated as Likes by its users. Our goal is twofold. First, we aim to examine the relation between the underlined social dynamic, as expressed indirectly by a community structure, with the users' characteristics represented by Likes. Second, to valuate the outcome of the community detection algorithm. To the best of our knowledge this is the first study of the correlation between community structure and users' Likes in Facebook. Using a standard crawling method, such as the Breadth First Search, we collect: a) several snapshots of a sub graph of Facebook, b) the users' Likes in Web and Facebook pages and c) the pages' categories as classified by the owner of the page. We study several graph samples along with their community structure. The experimental results demonstrate that in the case of users' Likes, the correlation ranges from small to medium between communities and the whole population, while it is even smaller between communities. Moreover, there is a high correlation in terms of Likes' categories between the different communities and between communities and the whole population. This fact proves that Likes constitute a criterion of distinction among the communities and verifies the intuition that lead us towards this research.
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016
The study of a large real world network in terms of graph sample representation constitutes a ver... more The study of a large real world network in terms of graph sample representation constitutes a very powerful and useful tool in several domains of network analysis. This is the motivation that has led the work of this paper towards the development of a new graph sampling algorithm. Previous research in this area proposed simple processes such as the classic Random Walk algorithm, Random node and Random edge sampling and has evolved during the last decade to more advanced graph exploration approaches such as Forest Fire and Frontier sampling. In this paper, we propose a new graph sampling method based on edge selection. In addition, we crawled Facebook collecting a large dataset consisting of 10 million users and 80 million users' relations, which we have also used to evaluate our sampling algorithm. The experimental evaluation on several datasets proves that our approach preserves several properties of the initial graphs, leading to representative samples and outperforms all the other approaches.
The study of a large real world network in terms of graph sample representation constitutes a ver... more The study of a large real world network in terms
of graph sample representation constitutes a very powerful and
useful tool in several domains of network analysis. This is
the motivation that has led the work of this paper towards
the development of a new graph sampling algorithm. Previous
research in this area proposed simple processes such as the
classic Random Walk algorithm, Random node and Random
edge sampling and has evolved during the last decade to more
advanced graph exploration approaches such as Forest Fire
and Frontier sampling. In this paper, we propose a new graph
sampling method based on edge selection. In addition, we crawled
Facebook collecting a large dataset consisting of 10 million users and 80 million users’ relations, which we have also used to evaluate our sampling algorithm. The experimental evaluation on several datasets proves that our approach preserves several properties of the initial graphs, leading to representative samples and outperforms all the other approaches.
In 2012 IEEE International Conference on Green Computing and Communications, GreenCom 2012, Besancon, France, November 20-23, 2012
We investigate the correlation between the social network communities as defined by a community d... more We investigate the correlation between the social network communities as defined by a community detection algorithm and the Facebook pages annotated as Likes by its users. Our goal is twofold. First, we aim to examine the relation between the underlined social dynamic, as expressed indirectly by a community structure, with the users' characteristics represented by Likes. Second, to valuate the outcome of the community detection algorithm. To the best of our knowledge this is the first study of the correlation between community structure and users' Likes in Facebook. Using a standard crawling method, such as the Breadth First Search, we collect: a) several snapshots of a sub graph of Facebook, b) the users' Likes in Web and Facebook pages and c) the pages' categories as classified by the owner of the page. We study several graph samples along with their community structure. The experimental results demonstrate that in the case of users' Likes, the correlation ranges from small to medium between communities and the whole population, while it is even smaller between communities. Moreover, there is a high correlation in terms of Likes' categories between the different communities and between communities and the whole population. This fact proves that Likes constitute a criterion of distinction among the communities and verifies the intuition that lead us towards this research.