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- Identifying and ranking super spreaders in real world complex networks without influence overlapPublication . Maji, Giridhar; Dutta, Animesh; Curado Malta, Mariana; Sen, SoumyaIn the present-days complex networks modeled on real-world data contain millions of nodes and billions of links. Identifying super spreaders in such an extensive network is a challenging task. Super spreaders are the most important or influential nodes in the network that play the central role during an infection spreading or infor mation diffusion process. Depending on the application, either the most influential node needs to be identified, or a set of initial seed nodes are identified that can maximize the collective influence or the total spread in the network. Many centrality measures have been proposed to rank nodes in a complex network such as ‘degree’, ‘closeness’, ‘betweenness’, ‘coreness’ or ‘k-shell’ centrality, among others. All have some kind of inherent limi tations. Mixed degree decomposition or m-shell is an improvement over k-shell that yields better ranking. Many researchers have employed single node identification heuristics to select multiple seed nodes by considering top-k nodes from the ranked list. This approach does not results in the optimal seed nodeset due to the considerable overlap in total spreading influence. Influence overlap occurs when multiple nodes from the seed nodeset in fluence a specific node, and it is counted multiple times during total collective influence computation. In this paper, we exploit the ‘node degree’, ‘closeness’ and ‘coreness’ among the nodes and propose novel heuristic template to rank the super spreaders in a network. We employ k-shell and m-shell as a coreness measure in two variants for a comparative evaluation. We use a geodesic-based constraint (enforcing a minimum distance between seed nodes) to select an initial seed nodeset from that ranked nodes for influence maximization instead of selecting the top-k nodes naively. All models and metrics are updated to avoid overlapping influence during total spread computation. Experimental simulation with the SIR (Susceptible-Infectious-Recovered) spreading model and an evaluation with performance metrics like spreadability, monotonicity of ranking, Kendall’s rank correlation on some benchmark real-world networks establish the superiority of the proposed methods and the improved seed node selection technique.
- Identifying and ranking super spreaders in real world complex networks without influence overlapPublication . Maji, Giridhar; Dutta, Animesh; Malta, Mariana Curado; Sen, SoumyaIn the present-days complex networks modeled on real-world data contain millions of nodes and billions of links. Identifying super spreaders in such an extensive network is a challenging task. Super spreaders are the most important or influential nodes in the network that play the central role during an infection spreading or infor-mation diffusion process. Depending on the application, either the most influential node needs to be identified, or a set of initial seed nodes are identified that can maximize the collective influence or the total spread in the network. Many centrality measures have been proposed to rank nodes in a complex network such as ‘degree’, ‘closeness’, ‘betweenness’, ‘coreness’ or ‘k-shell’ centrality, among others. All have some kind of inherent limi-tations. Mixed degree decomposition or m-shell is an improvement over k-shell that yields better ranking. Many researchers have employed single node identification heuristics to select multiple seed nodes by considering top- k nodes from the ranked list. This approach does not results in the optimal seed nodeset due to the considerable overlap in total spreading influence. Influence overlap occurs when multiple nodes from the seed nodeset in-fluence a specific node, and it is counted multiple times during total collective influence computation. In this paper, we exploit the ‘node degree’, ‘closeness’ and ‘coreness’ among the nodes and propose novel heuristic template to rank the super spreaders in a network. We employ k-shell and m-shell as a coreness measure in two variants for a comparative evaluation. We use a geodesic-based constraint (enforcing a minimum distance be-tween seed nodes) to select an initial seed nodeset from that ranked nodes for influence maximization instead of selecting the top-k nodes naively. All models and metrics are updated to avoid overlapping influence during total spread computation. Experimental simulation with the SIR (Susceptible-Infectious-Recovered) spreading model and an evaluation with performance metrics like spreadability, monotonicity of ranking, Kendall’s rank corre-lation on some benchmark real-world networks establish the superiority of the proposed methods and the improved seed node selection technique
- Developing a metadata application profile for the daily hire labourPublication . Sen, Sangeeta; Raza, Nishat; Dutta, Animesh; Malta, Mariana Curado; Baptista, Ana AliceEMPOWER SSE is a Fundação para a Ciência e Tecnologia (FCT, Portugal) and Department of Science & Technology (DST, India), financed research project that aims to use the Linked Open Data Framework to empower the Social and Solidarity Economy (SSE) Agents. It is a collaborative project between India and Portugal that is focused on defining a Semantic Web framework to consolidate players of the informal sector, enabling a paradigm shift. The Indian economy can be categorized into two sectors: formal and informal. The informal sector economy differs from the formal as it is an unorganized sector and comprised of economic activities that are not covered by formal arrangements such as taxation, labor protections, minimum wage regulations, unemployment benefits, or documentation. The major economy in India depends on the skilled labor of this informal sector such e.g. daily labor, farmers, electricians, food production, and small-scale industries (Kalyani, 2016). The informal sector is mainly made of skilled people that follow their family job traditions, sometimes they are not even formally trained. This sector struggles with the lack of information, data sharing needs and interoperability issues across systems and organizational boundaries. In fact, this sector does not have any visibility to the society not having the possibility to do business as most of the agents of this sector do not reach the end of the chain. This blocks them from getting proper exposure and a better livelihood.
- Influential spreaders identification in complex networks with improved k-shell hybrid methodPublication . Maji, Giridhar; Namtirtha, Amrita; Dutta, Animesh; Curado Malta, MarianaIdentifying influential spreaders in a complex network has practical and theoretical significance. In applications such as disease spreading, virus infection in computer networks, viral marketing, immunization, rumor containment, among others, the main strategy is to identify the influential nodes in the network. Hence many different centrality measures evolved to identify central nodes in a complex network. The degree centrality is the most simple and easy to compute whereas closeness and betweenness centrality are complex and more time-consuming. The k-shell centrality has the problem of placing too many nodes in a single shell. Over the time many improvements over k-shell have been proposed with prosand cons. The k-shell hybrid ( ksh ) method has been recently proposed with promising results but with a free parameter that is set empirically which may cause some constraints to the performance of the method. This paper presents an improvement of the ksh method by providing a mathematical model for the free parameter based on standard network parameters. Experiments on real and artificially generated networks show that the proposed method outperforms the ksh method and most of the state-of-the-art node indexing methods. It has a better performance in terms of ranking performance as measured by the Kendall’s rank correlation, and in terms of ranking efficiency as measured by the monotonicity value. Due to the absence of any empirically set free parameter, no time-consuming preprocessing is required for optimal parameter value selection prior to actual ranking of nodes in a large network.
- Influential spreaders identification in complex networks with improved k-shell hybrid methodPublication . Maji, Giridhar; Namtirtha, Amrita; Dutta, Animesh; Malta, Mariana CuradoIdentifying influential spreaders in a complex network has practical and theoretical significance. In appli- cations such as disease spreading, virus infection in computer networks, viral marketing, immunization, rumor containment, among others, the main strategy is to identify the influential nodes in the network. Hence many different centrality measures evolved to identify central nodes in a complex network. The degree centrality is the most simple and easy to compute whereas closeness and betweenness central- ity are complex and more time-consuming. The k-shell centrality has the problem of placing too many nodes in a single shell. Over the time many improvements over k-shell have been proposed with pros and cons. The k-shell hybrid ( ksh ) method has been recently proposed with promising results but with a free parameter that is set empirically which may cause some constraints to the performance of the method. This paper presents an improvement of the ksh method by providing a mathematical model for the free parameter based on standard network parameters. Experiments on real and artificially generated networks show that the proposed method outperforms the ksh method and most of the state-of-the-art node indexing methods. It has a better performance in terms of ranking performance as measured by the Kendall’s rank correlation, and in terms of ranking efficiency as measured by the monotonicity value. Due to the absence of any empirically set free parameter, no time-consuming preprocessing is required for optimal parameter value selection prior to actual ranking of nodes in a large network.
- State-of-the-art approaches for meta-knowledge assertion in the web of dataPublication . Sen, Sangeeta; Malta, Mariana Curado; Dutta, Biswanath; Dutta, AnimeshThe integration of meta-knowledge on the Web of data is essential to support trustworthiness. This is in fact an issue because of the enormous amount of data that exists on the Web of Data. Meta-knowledge describes how the data is generated, manipulated, and disseminated. In the last few years, several approaches have been proposed for tracing and representing meta-knowledge efficiently on a statement or on a set of statements in the Semantic Web. The approaches differ significantly; for instance, in terms of modelling patterns, the number of statements generation, redundancy of the resources, query length, or query response time. This article reports a systematic review of the various approaches of the four dimensions (namely time, trust, fuzzy, and provenance) to provide an overview of the meta-knowledge assertion techniques in the field of the Semantic Web. Some experiments are conducted to analyze the actual performance of the approaches of meta-knowledge assertion considering the provenance dimension. These experiments are based on specific parameters such as graph size, number of statements generation, redundancy, query length, and query response time. All the experiments are done with real-world datasets. The semantics of the different approaches are compared to analyze the methodology of the approaches. Our study and experiments highlight the advantages and limitations of the approaches in terms of the parameters mentioned above.