As a clustering approach based on density, Density Peaks Clustering algorithm (DPC) has
conspicuous superiorities in searching and finding density peaks. Nevertheless, DPC has …

Assortativity measures the tendency of a vertex in a network being connected by other
vertexes with respect to some vertex-specific features. Classical assortativity coefficients are …

The aim of this paper is to evaluate the ability of the line-graph based model (LGBM) to
assess the vulnerability of power systems as part of complex network theory. The nodes in …

In this paper, we propose to embed edges instead of nodes using state-of-the-art
neural/factorization methods (DeepWalk, node2vec, NetMF). These methods produce latent …

Key nodes identification is an important way to analyze and understand the characteristics,
structure, and functions of the complex network. In this paper, links in complex networks are …

We define several novel centrality metrics: the high‐order degree and combined degree of
undirected network, the high‐order out‐degree and in‐degree and combined out out …

In this paper, we presents a new method for extracting interest points from RGB images by
complex network analysis theory. Firstly, the RGB images are expressed as the multi-level …

In this paper we validate the use of degree centrality measures based on power traffic to
assess the vulnerability of power systems and to predict 70% brownouts and blackouts …

A distributed consensus algorithm for estimating the degree distribution of a graph is
proposed. The proposed algorithm is based on average consensus and in-network …

The degree distribution of a simulated Barabási–Albert graph under linear preferential
attachment is investigated. Specifically, the parameters of the power law distribution are …