We study walk-based centrality measures for time-ordered network sequences. For the case of standard dynamic walk-counting, we show how to derive and compute centrality measures induced by analytic functions. We also prove that dynamic Katz centrality, based on the resolvent function, has the unique advantage of allowing computations to be performed entirely at the node level. We then consider two distinct types of backtracking and develop a framework for capturing dynamic walk combinatorics when either or both is disallowed
The relative importance of nodes in a network can be quantified via functions of the adjacency matri...
We introduce and study a new network centrality measure based on the concept of nonbacktracking walk...
In many settings it is appropriate to treat the evolution of pairwise interactions over continuous t...
Funding Information: The work of F.A. was supported by fellowship ECF-2018-453 from the Leverhulme T...
We develop a comprehensive theory for the combinatorics of walk-counting on a directed graph in the ...
We describe a complete theory for walk-based centrality indices in complex networks defined in terms...
The theory of zeta functions provides an expression for the generating function of nonbacktracking w...
The theory of zeta functions provides an expression for the generating function of nonbacktracking w...
We derive an explicit formula for the exponential generating function associated with non-backtracki...
Walks around a graph are studied in a wide range of fields, from graph theory and stochastic analysi...
We introduce and study a new network centrality measure based on the concept of nonbacktracking walk...
In complex networks, centrality metrics quantify the connectivity of nodes and identify the most imp...
We propose a new algorithm for summarizing properties of large-scale time-evolving networks. This ty...
Walks around a graph are studied in a wide range of fields, from graph theory and stochastic analysi...
The relative importance of nodes in a network can be quantified via functions of the adjacency matri...
The relative importance of nodes in a network can be quantified via functions of the adjacency matri...
We introduce and study a new network centrality measure based on the concept of nonbacktracking walk...
In many settings it is appropriate to treat the evolution of pairwise interactions over continuous t...
Funding Information: The work of F.A. was supported by fellowship ECF-2018-453 from the Leverhulme T...
We develop a comprehensive theory for the combinatorics of walk-counting on a directed graph in the ...
We describe a complete theory for walk-based centrality indices in complex networks defined in terms...
The theory of zeta functions provides an expression for the generating function of nonbacktracking w...
The theory of zeta functions provides an expression for the generating function of nonbacktracking w...
We derive an explicit formula for the exponential generating function associated with non-backtracki...
Walks around a graph are studied in a wide range of fields, from graph theory and stochastic analysi...
We introduce and study a new network centrality measure based on the concept of nonbacktracking walk...
In complex networks, centrality metrics quantify the connectivity of nodes and identify the most imp...
We propose a new algorithm for summarizing properties of large-scale time-evolving networks. This ty...
Walks around a graph are studied in a wide range of fields, from graph theory and stochastic analysi...
The relative importance of nodes in a network can be quantified via functions of the adjacency matri...
The relative importance of nodes in a network can be quantified via functions of the adjacency matri...
We introduce and study a new network centrality measure based on the concept of nonbacktracking walk...
In many settings it is appropriate to treat the evolution of pairwise interactions over continuous t...