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Clustering Biological Data
Published in Charu C. Aggarwal, Chandan K. Reddy, Data Clustering, 2018
Chandan K. Reddy, Mohammad Al Hasan, Mohammed J. Zaki
CFinder is a free software for finding and visualizing overlapping dense groups of nodes in networks, based on the Clique Percolation Method (CPM) [1]. It is a fast program for locating and visualizing overlapping, densely interconnected groups of nodes in undirected graphs, and allowing the user to easily navigate between the original graph and the web of these groups. This software is available at http://www.cfinder.org/. The code for Markov clustering is available at http://www.micans.org/mcl/. It provides a collection of network analysis tools focused on analysis of very large networks, scaling up to millions of nodes and hundreds of millions of edges. The software for MCODE algorithm [9] is made available in the form of a Cytoscape plugin at http://baderlab.org/Software/MCODE. The network analysis tools (NeAT) [15] provides a user-friendly web access to a collection of modular tools for the analysis of networks (graphs) and clusters. It includes a set of tools that support basic graph operations and clustering algorithms. NeAT is designed to cope with large datasets and provides a flexible toolbox for analyzing biological networks stored in various databases or obtained from high-throughput experiments. This software is available at http://rsat.ulb.ac.be/neat/.
One Platform Rules All
Published in Kuan-Ching Li, Hai Jiang, Albert Y. Zomaya, Big Data Management and Processing, 2017
GraphX is an API on top of Apache Spark for graph data processing. Spark GraphX introduces Resilient Distributed Graph (RDG, an abstraction of Spark RDDs), which associates records with the vertices and edges of a graph. RDG helps data analysts perform graph operations through various expressive primitives. Developers can use these primitives to implement graph data analysis algorithms such as PageRank, in a few lines of code. The GraphX component supports many use cases such as recommendation and fraud detection.
Some New Graph Coloring Problems
Published in N. P. Shrimali, Nita H. Shah, Recent Advancements in Graph Theory, 2020
In the following discussion, we discuss the rainbow neighbourhood number of different operations and products of some familiar graph classes. For the terminology of graph operations, we refer to [2] and for graph products, refer to [9].
Reverse-degree-based topological indices of fullerene cage networks
Published in Molecular Physics, 2023
Ali Ahmad, Ali N. A. Koam, Muhammad Azeem
The topological index or descriptor of a graph is a function from the reverse-degree-based set to the real numbers such that: New neighbourhood degree-based topological descriptors under QSPR analysis found in [16], QSPR studies and usage of graph invariants [17], generalised results on the degree-based topological indices found in [18, 19], Graph entropies and topological characterisation of kekulene structure tessellations [20], in [21, 22], authors Using QSPR analysis, reduced neighbourhood degree-based topological indices on anti-cancer medications, symmetric division deg index new findings in [23], new topological indices found in [24], topological indices for titania nanotubes [25], topological descriptors of Linde type A zeolites based on relativistic distance [26], molecular topology-based new chirality descriptors [27, 28], distance on the reverse degree [29], a survey on newly developed indices found in [30], descriptors of tetrahedral sheets of clay minerals [31], supramolecular chain of different complexes [32], while metal-organic structure discussed in [33], graph operations and their topological indices found in [34]. For other parametric study of graph theory and chemical structures, we refer to see [35–38]. The topological descriptors of chemical structure have received a lot of attention in the literature, which may be found in [39–41]
Sombor indices of γ-sheet of boron clusters
Published in Molecular Physics, 2023
Eshrag Ali Refaee, Ali Ahmad, Muhammad Azeem
In 2021, the Sombor index was just released, and within a year, it drew researchers. There isn't much literature on this specific index. This Sombor index is explained in terms of specific graph operations and monogenic semigroup graphs (Ref. [27]). The Zagreb indices of graphs are explored in terms of the Sombor index. Some extremal graph features are also assessed in Ref. [24].