In this work, we propose a measure that aims at assessing the position of a node with respect to the interconnected groups of nodes existing in a network. In particular, since the nodes of a network can be placed at different distances from cohesive groups, we extend the standard concept of clustering coefficient and provide the local l-adjacency clustering coefficient of a node i as an opportunely weighted mean of the clustering coefficients of nodes which are at distance l from i. Thus, the standard clustering coefficient is a peculiar local l-adjacency clustering coefficient for . As l varies, the local l-adjacency clustering coefficient is then used to infer insights on the position of each node in the overall structure. Empirical exper...
Several definitions of clustering coefficient for weighted networks have been proposed in literature...
In this chapter, we are going to present a number of techniques for detecting cohesive groups in net...
The structure of many complex networks includes edge directionality and weights on top of their topo...
In this work, we propose a measure that aims at assessing the position of a node with respect to the...
In this work, we propose a measure that aims at assessing the position of a node with respect to th...
Bipartite networks have gained an increasing amount of attention over the past few years. Network me...
Based on an expert systems approach, the issue of community detection can be conceptualized as a clu...
Based on an expert systems approach, the issue of community detection can be conceptualized as a clu...
In this paper, we provide novel definitions of clustering coefficient for weighted and directed mult...
Clustering is a central concept in network theory. Nevertheless, its usual formulation as the cluste...
Specific properties emerge from the structure of large networks, such as that of worldwide air traff...
The identification of influential nodes in complex network can be very challenging. If the network h...
Many real networks exhibit parts that are more tightly connected than others. The goal of network cl...
18 pages, 12 figuresWe propose a natural generalization of the well-known clustering coefficient for...
We develop a full theoretical approach to clustering in complex networks. A key concept is introduce...
Several definitions of clustering coefficient for weighted networks have been proposed in literature...
In this chapter, we are going to present a number of techniques for detecting cohesive groups in net...
The structure of many complex networks includes edge directionality and weights on top of their topo...
In this work, we propose a measure that aims at assessing the position of a node with respect to the...
In this work, we propose a measure that aims at assessing the position of a node with respect to th...
Bipartite networks have gained an increasing amount of attention over the past few years. Network me...
Based on an expert systems approach, the issue of community detection can be conceptualized as a clu...
Based on an expert systems approach, the issue of community detection can be conceptualized as a clu...
In this paper, we provide novel definitions of clustering coefficient for weighted and directed mult...
Clustering is a central concept in network theory. Nevertheless, its usual formulation as the cluste...
Specific properties emerge from the structure of large networks, such as that of worldwide air traff...
The identification of influential nodes in complex network can be very challenging. If the network h...
Many real networks exhibit parts that are more tightly connected than others. The goal of network cl...
18 pages, 12 figuresWe propose a natural generalization of the well-known clustering coefficient for...
We develop a full theoretical approach to clustering in complex networks. A key concept is introduce...
Several definitions of clustering coefficient for weighted networks have been proposed in literature...
In this chapter, we are going to present a number of techniques for detecting cohesive groups in net...
The structure of many complex networks includes edge directionality and weights on top of their topo...