Complex networks have emerged as a simple yet powerful framework to represent and
analyze a wide range of complex systems. The problem of ranking the nodes and the edges …

Numerous centrality measures have been developed to quantify the importances of nodes in
time-independent networks, and many of them can be expressed as the leading eigenvector …

Applied network science often involves preprocessing network data before applying a
network-analysis method, and there is typically a theoretical disconnect between these …

The theory of zeta functions provides an expression for the generating function of non-
backtracking walk counts on a directed network. We show how this expression can be used …

Graph distance and graph embedding are two fundamental tasks in graph mining. For graph
distance, determining the structural dissimilarity between networks is an ill-defined problem …

Many real-world applications based on spreading processes in complex networks aim to
deliver information to specific target nodes. However, it remains challenging to optimally …

We derive an explicit formula for the exponential generating function associated with non-
backtracking walks around a graph. We study both undirected and directed graphs. Our …

The PageRank algorithm, which has been “bringing order to the web” for more than 20
years, computes the steady state of a classical random walk plus teleporting. Here we …

A second-order random walk on a graph or network is a random walk where transition
probabilities depend not only on the present node but also on the previous one. A notable …

Non-backtracking PageRank is a variation of Google's PageRank, which is based on non-
backtracking random walk. However, if the number of dangling nodes of a graph is large, the …