21 papers · ranked by Valyu relevance
Komal Batool, Muaz A. Niazi, Matjaž Perc
Background Living systems are associated with Social networks - networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential…
Minoo Ashtiani, Mehdi Mirzaie, Mohieddin Jafari
In network science, usually there is a critical step known as centrality analysis. This is an important step, since by using centrality measures, a large number of vertices with low priority are set aside and only a few ones remain to be used for further inferential outcomes. In the other words, these measures help us…
Minoo Ashtiani, Mehdi Mirzaie, Zahra Razaghi-Moghadam, Holger Hennig + 3 more
In network science, although different types of centrality measures have been introduced to determine important nodes of networks, a consensus pipeline to select and implement the best tailored measure for each complex network is still an open field. In the present study, we examine the node centrality profiles of…
Giovanni Scardoni, Gabriele Tosadori, Mohammed Faizan, Fausto Spoto + 2 more
'Franco Fabbri' 'Carlo Laudanna'] The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate…
Sergey Shvydun
| | Preface. | | 12 | |---|----------|----------------------------------------------------------------------------------------------------------------|----| | 1 | | Centrality in Complex Networks | 14 | | | 1.1 | Introduction | 14 | | | 1.2 | Notation | 17 | | | 1.3 | Classification of centrality measures | 19 | | | |…
Frédérique Oggier, Silivanxay Phetsouvanh, Anwitaman Datta, Diego Amancio
'Diego Amancio'] The notion of entropic centrality measures how central a node is in terms of how uncertain the destination of a flow starting at this node is: the more uncertain the destination, the more well connected and thus central the node is deemed. This implicitly assumes that the flow is indivisible, and at…
A. J. Alvarez-Socorro, G. C. Herrera-Almarza, L. A. González-Díaz
One of the most important problems in complex network’s theory is the location of the entities that are essential or have a main role within the network. For this purpose, the use of dissimilarity measures (specific to theory of classification and data mining) to enrich the centrality measures in complex networks is…
Carla Sciarra, Guido Chiarotti, Francesco Laio, Luca Ridolfi
Typing “Yesterday” into the search-bar of your browser provides a long list of websites with, in top places, a link to a video by The Beatles. The order your browser shows its search results is a notable example of the use of network centrality. Centrality measures the importance of the nodes in a network and it plays…
Francisco A. Rodrigues
Centrality is a key property of complex networks that influences the behavior of dynamical processes, like synchronization and epidemic spreading, and can bring important information about the organization of complex systems, like our brain and society. There are many metrics to quantify the node centrality in…
Akrati Saxena, S. R. S. Iyengar
| 1 | Introduction | | 3 | | --- | --- | --- | --- | | 2 | Preliminaries | | 7 | | | 2.1 | Definitions | 10 | | 3 | | Degree Centrality | 11 | | | 3.1 | Extensions | 12 | | | 3.2 | Identify Top-k Nodes | 16 | | | 3.3 | Ranking | 16 | | | 3.4 | Applications | 17 | | 4 | | Closeness Centrality | 18 | | | 4.1 | Extensions…
Alexandre Bovet, Hernán A. Makse
Network A network is a collection of nodes (also called vertices) and edges (also called links) linking pair of nodes. Mathematically, it is represented by a graph G = (V, E) where V is the set of nodes and E ⊆ V × V is the set of edges. Additional information can be attached to each node or edge, for example edges can…
Abbas Salavaty, Mirana Ramialison, Peter D Currie
Biological systems are composed of highly complex networks and decoding the functional significance of individual network components is critical for understanding healthy and diseased states. Several algorithms have been designed to identify the most influential regulatory points, or hub nodes, within a network.…
Ubaida Fatima, Saman Hina, Muhammad Wasif
This study introduces “Dangling Centrality,” a novel metric for identifying critical nodes in networks by assessing the impact of their link removal on system dynamics. The proposed metric is validated on real-world datasets, including Amazon product networks, a Protein-Protein Interaction (PPI) network, and a Bitcoin…
Andrea Costa, Ana M. Martín González, Katell Guizien, Andrea M. Doglioli + 3 more
Representing data as networks cuts across all sub-disciplines in ecology and evolutionary biology. Besides providing a compact representation of the interconnections between agents, network analysis allows the identification of especially important nodes, according to various metrics of network centrality. These…
Rishi Ranjan Singh
Experts from several disciplines have been widely using centrality measures for analyzing large as well as complex networks. These measures rank nodes/edges in networks by quantifying a notion of the importance of nodes/edges. Ranking aids in identifying important and crucial actors in networks. In this chapter, we…
Samin Aref
Network centralization, driven by hub nodes, impacts communication efficiency, structural integration, and dynamic processes such as diffusion and synchronization. Although numerous centralization measures exist, a major challenge lies in determining measures that are both theoretically sound and empirically reliable…
Majid Saberi, Samin Aref, Amit Nanavati
Network centralization, driven by hub nodes, impacts communication efficiency, structural integration, and dynamic processes such as diffusion and synchronization. Although numerous centralization measures exist, a major challenge lies in determining measures that are both theoretically sound and empirically reliable…
Jana Weber, Pietro Lio’, Alexei Lapkin
Networks of chemical reactions represent relationships between molecules within chemical supply chains and promise to enhance planning of multi-step synthesis routes from bio-renewable feedstocks. This study aims to identify strategic molecules in chemical reaction networks that may potentially play a significant role…
Alexandr P. Kornev, Phillip C. Aoto, Susan S. Taylor
Topological analysis of amino acid networks is a common method that can help to understand the roles of individual residues. The most popular approach for network construction is to create a connection between residues if they interact. These interactions are usually weighted by absolute values of correlation…
Felix Brandt, Christoph Jacob
The construction of a suitable QM region is the most crucial step in setting up hybrid quantum mechanics / molecular mechanics (QM/MM) simulations for enzymatic reactions. The QM region should ideally include all important amino acids residues, while being as small as possible to save computational effort. Most…
Authors not listed
Identifying synthesis routes from knowledge graphs poses challenges beyond retrosynthesis, including path–finding artifacts and data issues. We introduce “SynGPS”, a novel algorithm that overcomes these limitations by identifying viable routes even with common artifacts. SynGPS can resolve nonsensical cycles…