A note on a generalization of eigenvector centrality for bipartite graphs and applications

Daugulis, Peteris

doi:10.1002/net.20442

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…The eigenvector centrality of a node is proportional to the sum of centralities of the nodes it is adjacent to. Bipartite eigenvector centrality is further reviewed by Daugulis [40].…”

Section: Bipartite Graphsmentioning

confidence: 99%

Bipartite graphs in systems biology and medicine: a survey of methods and applications

et al. 2018

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The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

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“…The eigenvector centrality of a node is proportional to the sum of centralities of the nodes it is adjacent to. Bipartite eigenvector centrality is further reviewed by Daugulis [40].…”

Section: Bipartite Graphsmentioning

confidence: 99%

Bipartite graphs in systems biology and medicine: a survey of methods and applications

et al. 2018

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“…In most cases, the more important the task is, the more it can highlight the value of the worker from the side. Eigenvector centrality has been widely used in webpage ranking for search engine [45] and other rating systems [20], [45], [46]. Recently, Daugulis proposed a generalized eigenvector centrality to rank the nodes in the bipartite graph [20].…”

Section: ) Centrality-based Attackmentioning

confidence: 99%

“…Eigenvector centrality has been widely used in webpage ranking for search engine [45] and other rating systems [20], [45], [46]. Recently, Daugulis proposed a generalized eigenvector centrality to rank the nodes in the bipartite graph [20]. If we define rating vectors x and y for tasks and workers, respectively, and also an adjacency matrix representing the allocation of tasks to workers, the following assumptions are then made to create a rating system [20]:…”

Section: ) Centrality-based Attackmentioning

confidence: 99%

“…It not only has important theoretical research significance [16], but also has a wide range of practical applications [17]- [19]. Ranking in bipartite network has its practical significance, which can be applied to the following examples: problems and solvers, products and consumers [20]. We consider the eigenvector centrality [20] to evaluate the importance of a worker in the bipartite network.…”

Section: Introductionmentioning

confidence: 99%

“…Ranking in bipartite network has its practical significance, which can be applied to the following examples: problems and solvers, products and consumers [20]. We consider the eigenvector centrality [20] to evaluate the importance of a worker in the bipartite network. And then the diversity index is also used to evaluate the worker, which is usually used for ecological species [21], [22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robustness Analysis of Bipartite Task Assignment Networks: A Case Study in Hospital Logistics System

Xuan

Chen

et al. 2019

IEEE Access

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Task-oriented social systems involve workers and tasks, which can typically be modeled by bipartite networks. The absence of workers may cause some tasks cannot be finished in time, thus hampering the smoothing operation of the whole system. In this study, we focus on the robustness analysis of such bipartite task-oriented social networks under the absence of some workers. In particular, taking hospital logistics systems as an example, we propose four attack strategies: efficiency-based attack, centrality-based attack, diversity-based attack, and influence-based attack. The experiments on nine real hospital logistics systems show that these systems are especially vulnerable to the diversity-based attack. Different hospitals may present different patterns: some hospital logistics systems are relatively robust against these attacks while others are more vulnerable. The former hospitals are relatively small in size and adopt a global task assignment mode in which the workers can be easily replaced by others. On the contrary, the workers in the latter ones are highly professional and irreplaceable due to the large scale of the hospitals and the adoption of a regional task assignment mode. We also design two task reassignment mechanisms to model the cascading failure of such systems. In this case, the working load plays an important role in the robustness of these systems. Moreover, we find that the robustness and efficiency seem to be negatively correlated with each other, and how to balance the two in real systems still remains an open problem.

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TriAssetRank: Ranking Vulnerabilities, Exploits, and Privileges for Countermeasures Prioritization

Tchimwa Bouom,

Lienou,

Ejuh Geh

et al. 2024

IEEE Trans.Inform.Forensic Secur.

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A note on a generalization of eigenvector centrality for bipartite graphs and applications

Cited by 5 publications

References 5 publications

Bipartite graphs in systems biology and medicine: a survey of methods and applications

Bipartite graphs in systems biology and medicine: a survey of methods and applications

Robustness Analysis of Bipartite Task Assignment Networks: A Case Study in Hospital Logistics System

TriAssetRank: Ranking Vulnerabilities, Exploits, and Privileges for Countermeasures Prioritization

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