Results 51 to 60 of about 2,949,927 (358)
aMatReader: Importing adjacency matrices via Cytoscape Automation [version 1; referees: 2 approved]
F1000Research, 2018 Adjacency matrices are useful for storing pairwise interaction data, such as correlations between gene pairs in a pathway or similarities between genes and conditions. The aMatReader app enables users to import one or multiple adjacency matrix files into
Brett Settle +3 more
doaj +1 more source
Incidence matrices and line graphs of mixed graphs
Special Matrices, 2023 In the theory of line graphs of undirected graphs, there exists an important theorem linking the incidence matrix of the root graph to the adjacency matrix of its line graph. For directed or mixed graphs, however, there exists no analogous result.
Abudayah Mohammad +2 more
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Graph Spectral Properties of Deterministic Finite Automata
, 2014 We prove that a minimal automaton has a minimal adjacency matrix rank and a minimal adjacency matrix nullity using equitable partition (from graph spectra theory) and Nerode partition (from automata theory). This result naturally introduces the notion of
A. Goldberg +5 more
core +1 more source
aMatReader: Importing adjacency matrices via Cytoscape Automation [version 2; referees: 2 approved]
F1000Research, 2018 Adjacency matrices are useful for storing pairwise interaction data, such as correlations between gene pairs in a pathway or similarities between genes and conditions. The aMatReader app enables users to import one or multiple adjacency matrix files into
Brett Settle +3 more
doaj +1 more source
On the Adjacency, Laplacian, and Signless Laplacian Spectrum of Coalescence of Complete Graphs
Journal of Mathematics, 2016 Coalescence as one of the operations on a pair of graphs is significant due to its simple form of chromatic polynomial. The adjacency matrix, Laplacian matrix, and signless Laplacian matrix are common matrices usually considered for discussion under ...
S. R. Jog, Raju Kotambari
doaj +1 more source
Deep Learning Techniques for Community Detection in Social Networks
IEEE Access, 2020 Graph embedding is an effective yet efficient way to convert graph data into a low dimensional space. In recent years, deep learning has applied on graph embedding and shown outstanding performance.
Ling Wu +4 more
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On α-adjacency energy of graphs and Zagreb index
AKCE International Journal of Graphs and Combinatorics, 2021 Let A(G) be the adjacency matrix and D(G) be the diagonal matrix of the vertex degrees of a simple connected graph G. Nikiforov defined the matrix of the convex combinations of D(G) and A(G) as for If are the eigenvalues of (which we call α-adjacency ...
S. Pirzada +3 more
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FEBS Letters, EarlyView.Phototrophs evolved light‐harvesting systems adapted for efficient photon capture in habitats enriched in far‐red radiation. A subset of eukaryotic pigment‐binding proteins can absorb far‐red photons via low‐energy chlorophyll states known as red forms.
Antonello Amelii +8 more
wiley +1 more source
KAMG: A Tool for Converting Blood Ties and Affinity Ties into Adjacency Matrices
Journal of Open Research Software, 2016 Kinship Adjacency Matrix Generator (KAMG) is a browser-based software for creating adjacency matrices using the information of kinship ties. Specifically, it is capable of converting the family trees in the format of GEDCOM files into adjacency matrices ...
Hang Xiong, Pin Xiong, Hui Xiong
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An algebraic analysis of the graph modularity
, 2014 One of the most relevant tasks in network analysis is the detection of community structures, or clustering. Most popular techniques for community detection are based on the maximization of a quality function called modularity, which in turn is based upon
Fasino, Dario, Tudisco, Francesco
core +1 more source