Nikos Salamanos - Academia.edu (original) (raw)

Papers by Nikos Salamanos

The problem of identifying the influential spreaders - the important nodes - in a real world netw... more The problem of identifying the influential spreaders - the important nodes - in a real world network is of high importance due to its theoretical interest as well as its practical applications, such as the acceleration of information diffusion, the control of the spread of a disease and the improvement of the resilience of networks to external
attacks. In this paper, we propose a graph exploration sampling method that accurately identifies the influential spreaders in a complex network, without any prior knowledge of the original graph, apart from the collected samples/subgraphs. The method explores the graph, following a deterministic selection rule and outputs a graph sample - the set of edges that have been crossed. The proposed method is based on a
version of Rank Degree graph sampling algorithm. We conduct extensive experiments in eight real world networks by simulating the susceptible-infected-recovered(SIR) and susceptible-infected-susceptible(SIS) epidemic models which serve as ground truth identifiers of nodes spreading efficiency. Experimentally, we show that by exploring
only the 20% of the network and using the degree centrality as well as the k-core measure, we are able to identify the influential spreaders with at least the same accuracy as in the full information case, namely, the case where we have access to the original graph and in that graph, we compute the centrality measures. Finally and more importantly, we present strong evidence that the degree centrality - the degree of nodes in the collected samples - is almost as accurate as the k-core values obtained from the original graph.

Soc. Netw. Anal. Min. (2017) 7: 24. doi:10.1007/s13278-017-0441-6, May 6, 2017

Graph sampling is a widely used procedure in social network analysis, has attracted great interes... more Graph sampling is a widely used procedure in social network analysis, has attracted great interest in the scientific community and is considered as a very powerful and useful tool in several domains of network analysis. Apart from initial research in this area, which has proposed simple processes such as the classic Random Walk algorithm, Random Node and Random Edge sampling, during the last decade, more advanced graph sampling approaches have been emerged. In this paper, we extensively study the properties of a newly proposed method, the Rank Degree method, which leads to representative graph subgraphs. The Rank Degree is a novel graph exploration method which significantly differs from other existing methods in the literature. The novelty of the Rank Degree lies on the fact that its core methodology corresponds to a deterministic graph exploration; one specific variation corresponds to a number of parallel deterministic traverses that explore the graph. We perform extensive experiments on twelve real world datasets of a different type, using a variety of measures and comparing our method with Forest Fire, Metropolis Hastings Random Walk and Metropolis Hastings. We provide strong evidence that our approach leads to highly efficient graph sampling; the generated samples preserve several graph properties, to a large extent.

Salamanos N, Voudigari E, Yannakoudakis EJ (2016) Identifying influential spreaders by graph sampling. In: Proceedings of the 5th International Workshop on Complex Networks and their Applications, Milan, Italy, November 30 - December 02, 2016

The complex nature of real world networks is a central subject in several disciplines, from Physi... more The complex nature of real world networks is a central subject in several
disciplines, from Physics to computer science. The complex network dynamics of
peers communication and information exchange are specified to a large degree by
the most efficient spreaders - the entities that play a central role in various ways such
as the viruses propagation, the diffusion of information, the viral marketing and net-
work vulnerability to external attacks. In this paper, we deal with the problem of
identifying the influential spreaders of a complex network when either the network
is very large or else we have limited computational capabilities to compute global
centrality measures. Our approach is based on graph sampling and specifically on
Rank Degree, a newly published graph exploration sampling method. We conduct
extensive experiments in five real world networks using four centrality metrics for
the nodes spreading efficiency. We present strong evidence that our method is highly
effective. By sampling 30% of the network and using at least two out of four centrality measures, we can identify more than 80% of the influential spreaders, while
at the same time, preserving the original ranking to a large extent.

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.

Int. J. Knowledge and Learning, Vol. 8, Nos. 3/4, pp.259–281, 2012, 2012

We study the effect of diffusion on the evolution of a market consisting of two infinitely divisi... more We study the effect of diffusion on the evolution of a market consisting of two infinitely divisible goods and buyers with constant elasticity of substitution utility functions. In consecutive time periods, the buyers’ preferences depend on the actions taken by their neighbours in the network. We investigate the properties of the long time states, where a market state is defined by the market equilibrium prices and goods allocation. The experimental results demonstrate that the long time states are sensitive to initial conditions and exhibit the following patterns. Homogeneous: the market prices of the two goods are equal and the buyers split equally their budget amongst the goods. Heterogeneous: the buyers’ bids on the two goods differ. Periodic: the buyers’ bids oscillate with stable oscillation width. Moreover, we present the critical values where a phase transition occurs between homogeneous, heterogeneous and periodic states.

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.

In Proceedings of the Third International Conference on Intelligent Networking and Collaborative Systems (INCoS-2011), IEEE 2011, Fukuoka Institute of Technology, Fukuoka, Japan, Nov 2011, 2011

We propose a model of the evolution of a market with linear utilities in the presence of both loc... more We propose a model of the evolution of a market with linear utilities in the presence of both local and global social interactions. In the scenario considered, there is a market consisting of buyers and divisible goods. In consecutive time periods, the decision of a buyer is affected by the consumption plan of his neighbors and by a global signal, the distribution of actions of all agents. Moreover, we assume that the market prices and the allocation of the goods are stabilized by the law of supply and demand. We simulate the model, along with a market equilibria algorithm, and we investigate the long time behavior of the system. Specifically, we analyze the distribution of the prices and the market share of the products, when the configuration of the network is Erdos-Renyi and Scale-free graph. The experimental results show that the long time behavior of the system is not always static. The long time states depict a periodic pattern and are sensitive to a) the initial agents' beliefs, b) the weights that each agent assigns to the local and the global factor respectively and c)the degree distribution of the nodes in the network.

In Proceedings of the 28th Annual International Conference on Design of Communication, SIGDOC 2010, Sao Carlos, Sao Paulo, Brazil, September 26-29, 2010, 2010

The concept of social networks in conjunction with concepts from economics has attracted consider... more The concept of social networks in conjunction with concepts from economics has attracted considerable attention in recent years. In this paper we propose the Stochastic Diffusion Market Search (SDMS), a novel contextual advertising method for mutual advertisement hosting among participating entities, where each owns a web site. In the scenario considered each participating agent/web-site buys or sells advertising links. In the proposed method the advertising market and network that formed into the system emerge from agents preferences and their social behavior into the network. SDMS consists of a variation of Stochastic Diffusion Search, a swarm intelligence metaheuristic, and an algorithm for market equilibria. We present an evaluation of the model and the experimental results show that the network potentially converges to a stable stage and the distribution of market prices adheres to power law properties.

The problem of identifying the influential spreaders - the important nodes - in a real world netw... more The problem of identifying the influential spreaders - the important nodes - in a real world network is of high importance due to its theoretical interest as well as its practical applications, such as the acceleration of information diffusion, the control of the spread of a disease and the improvement of the resilience of networks to external
attacks. In this paper, we propose a graph exploration sampling method that accurately identifies the influential spreaders in a complex network, without any prior knowledge of the original graph, apart from the collected samples/subgraphs. The method explores the graph, following a deterministic selection rule and outputs a graph sample - the set of edges that have been crossed. The proposed method is based on a
version of Rank Degree graph sampling algorithm. We conduct extensive experiments in eight real world networks by simulating the susceptible-infected-recovered(SIR) and susceptible-infected-susceptible(SIS) epidemic models which serve as ground truth identifiers of nodes spreading efficiency. Experimentally, we show that by exploring
only the 20% of the network and using the degree centrality as well as the k-core measure, we are able to identify the influential spreaders with at least the same accuracy as in the full information case, namely, the case where we have access to the original graph and in that graph, we compute the centrality measures. Finally and more importantly, we present strong evidence that the degree centrality - the degree of nodes in the collected samples - is almost as accurate as the k-core values obtained from the original graph.

Soc. Netw. Anal. Min. (2017) 7: 24. doi:10.1007/s13278-017-0441-6, May 6, 2017

Graph sampling is a widely used procedure in social network analysis, has attracted great interes... more Graph sampling is a widely used procedure in social network analysis, has attracted great interest in the scientific community and is considered as a very powerful and useful tool in several domains of network analysis. Apart from initial research in this area, which has proposed simple processes such as the classic Random Walk algorithm, Random Node and Random Edge sampling, during the last decade, more advanced graph sampling approaches have been emerged. In this paper, we extensively study the properties of a newly proposed method, the Rank Degree method, which leads to representative graph subgraphs. The Rank Degree is a novel graph exploration method which significantly differs from other existing methods in the literature. The novelty of the Rank Degree lies on the fact that its core methodology corresponds to a deterministic graph exploration; one specific variation corresponds to a number of parallel deterministic traverses that explore the graph. We perform extensive experiments on twelve real world datasets of a different type, using a variety of measures and comparing our method with Forest Fire, Metropolis Hastings Random Walk and Metropolis Hastings. We provide strong evidence that our approach leads to highly efficient graph sampling; the generated samples preserve several graph properties, to a large extent.

Salamanos N, Voudigari E, Yannakoudakis EJ (2016) Identifying influential spreaders by graph sampling. In: Proceedings of the 5th International Workshop on Complex Networks and their Applications, Milan, Italy, November 30 - December 02, 2016

The complex nature of real world networks is a central subject in several disciplines, from Physi... more The complex nature of real world networks is a central subject in several
disciplines, from Physics to computer science. The complex network dynamics of
peers communication and information exchange are specified to a large degree by
the most efficient spreaders - the entities that play a central role in various ways such
as the viruses propagation, the diffusion of information, the viral marketing and net-
work vulnerability to external attacks. In this paper, we deal with the problem of
identifying the influential spreaders of a complex network when either the network
is very large or else we have limited computational capabilities to compute global
centrality measures. Our approach is based on graph sampling and specifically on
Rank Degree, a newly published graph exploration sampling method. We conduct
extensive experiments in five real world networks using four centrality metrics for
the nodes spreading efficiency. We present strong evidence that our method is highly
effective. By sampling 30% of the network and using at least two out of four centrality measures, we can identify more than 80% of the influential spreaders, while
at the same time, preserving the original ranking to a large extent.

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.

Int. J. Knowledge and Learning, Vol. 8, Nos. 3/4, pp.259–281, 2012, 2012

We study the effect of diffusion on the evolution of a market consisting of two infinitely divisi... more We study the effect of diffusion on the evolution of a market consisting of two infinitely divisible goods and buyers with constant elasticity of substitution utility functions. In consecutive time periods, the buyers’ preferences depend on the actions taken by their neighbours in the network. We investigate the properties of the long time states, where a market state is defined by the market equilibrium prices and goods allocation. The experimental results demonstrate that the long time states are sensitive to initial conditions and exhibit the following patterns. Homogeneous: the market prices of the two goods are equal and the buyers split equally their budget amongst the goods. Heterogeneous: the buyers’ bids on the two goods differ. Periodic: the buyers’ bids oscillate with stable oscillation width. Moreover, we present the critical values where a phase transition occurs between homogeneous, heterogeneous and periodic states.

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.

In Proceedings of the Third International Conference on Intelligent Networking and Collaborative Systems (INCoS-2011), IEEE 2011, Fukuoka Institute of Technology, Fukuoka, Japan, Nov 2011, 2011

We propose a model of the evolution of a market with linear utilities in the presence of both loc... more We propose a model of the evolution of a market with linear utilities in the presence of both local and global social interactions. In the scenario considered, there is a market consisting of buyers and divisible goods. In consecutive time periods, the decision of a buyer is affected by the consumption plan of his neighbors and by a global signal, the distribution of actions of all agents. Moreover, we assume that the market prices and the allocation of the goods are stabilized by the law of supply and demand. We simulate the model, along with a market equilibria algorithm, and we investigate the long time behavior of the system. Specifically, we analyze the distribution of the prices and the market share of the products, when the configuration of the network is Erdos-Renyi and Scale-free graph. The experimental results show that the long time behavior of the system is not always static. The long time states depict a periodic pattern and are sensitive to a) the initial agents' beliefs, b) the weights that each agent assigns to the local and the global factor respectively and c)the degree distribution of the nodes in the network.

In Proceedings of the 28th Annual International Conference on Design of Communication, SIGDOC 2010, Sao Carlos, Sao Paulo, Brazil, September 26-29, 2010, 2010

The concept of social networks in conjunction with concepts from economics has attracted consider... more The concept of social networks in conjunction with concepts from economics has attracted considerable attention in recent years. In this paper we propose the Stochastic Diffusion Market Search (SDMS), a novel contextual advertising method for mutual advertisement hosting among participating entities, where each owns a web site. In the scenario considered each participating agent/web-site buys or sells advertising links. In the proposed method the advertising market and network that formed into the system emerge from agents preferences and their social behavior into the network. SDMS consists of a variation of Stochastic Diffusion Search, a swarm intelligence metaheuristic, and an algorithm for market equilibria. We present an evaluation of the model and the experimental results show that the network potentially converges to a stable stage and the distribution of market prices adheres to power law properties.