Add to ORKGA network measure called knotty-centrality is defined that quantifies the extent to which a given subset of a graph’s nodes constitutes a densely intra-connected topologically central connective core. Using this measure, the knotty centre of a network is defined as a sub-graph with maximal knotty-centrality. A heuristic algorithm for finding subsets of a network with high knotty-centrality is presented, and this is applied to previously published brain structural connectivity data for the cat and the human, as well as to a number of other networks. The cognitive implications of possessing a connective core with high knotty-centrality are briefly discussed
International audienceFinding the common structural brain connectivity network for a given populatio...
<p>For each measure of centrality sets of folds with high and low centralities are identified as des...
International audienceWe present a graph-theoretical algorithm to extract the connected core structu...
A network measure called knotty-centrality is defined that quantifies the extent to which a given su...
<div><p>A network measure called knotty-centrality is defined that quantifies the extent to which a ...
Recent developments in network theory have allowed for the study of the structure and function of th...
The centrality of an edge in a graph is proposed to be the degree of sensitivity of a graph distance...
Network structure analysis plays an important role in characterizing complex systems. Different from...
Krivelevich and Patkós conjectured in 2009 that χ(G(n, p)) ∼ χ=(G(n, p)) ∼ χ∗=(G(n, p)) for C/n <...
Living systems are associated with Social networks — networks made up of nodes, some of which may be...
Typical centrality measures assess the importance of a node based on the distances to other nodes, s...
<p>There is good agreement in this case between rich club membership, high betweenness centrality, a...
Abstract—Given a social network, which of its nodes are more central? This question was asked many t...
Abstract—Estimating influential nodes in large scale networks including but not limited to social ne...
International audienceFinding nodes occupying interesting positions in a graph is useful to extract ...
International audienceFinding the common structural brain connectivity network for a given populatio...
<p>For each measure of centrality sets of folds with high and low centralities are identified as des...
International audienceWe present a graph-theoretical algorithm to extract the connected core structu...
A network measure called knotty-centrality is defined that quantifies the extent to which a given su...
<div><p>A network measure called knotty-centrality is defined that quantifies the extent to which a ...
Recent developments in network theory have allowed for the study of the structure and function of th...
The centrality of an edge in a graph is proposed to be the degree of sensitivity of a graph distance...
Network structure analysis plays an important role in characterizing complex systems. Different from...
Krivelevich and Patkós conjectured in 2009 that χ(G(n, p)) ∼ χ=(G(n, p)) ∼ χ∗=(G(n, p)) for C/n <...
Living systems are associated with Social networks — networks made up of nodes, some of which may be...
Typical centrality measures assess the importance of a node based on the distances to other nodes, s...
<p>There is good agreement in this case between rich club membership, high betweenness centrality, a...
Abstract—Given a social network, which of its nodes are more central? This question was asked many t...
Abstract—Estimating influential nodes in large scale networks including but not limited to social ne...
International audienceFinding nodes occupying interesting positions in a graph is useful to extract ...
International audienceFinding the common structural brain connectivity network for a given populatio...
<p>For each measure of centrality sets of folds with high and low centralities are identified as des...
International audienceWe present a graph-theoretical algorithm to extract the connected core structu...