Similarity measureComplex networksCommunity detection is to detect groups consisting of densely connected nodes, and having sparse connections between them. Many researchers indicate that detecting community st
Although this combination of ‘popularity’ and ‘similarity’ is an attractive proposition, and one that will be echoed in the theory of this paper, these works do not provide an explanation for how the degree distributions of complex networks themselves arise. The literature suggests two major ...
This is a tunable measure for analysing the similarity of nodes across different link weights within a multilayer network. Similarly, Yuvaraj et al. [29] presented a per- spective on multilayer network clustering, using the machinery of topological data analysis to group nodes based on how ...
In some works, structural identity, i.e., a concept of symmetry, is also leveraged to measure similarity between two nodes. For instance, Figueiredo et al. (2017) proposed struc2vec, a novel representation learning framework for capturing structural identity of nodes in a network in such way...
Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These...
Likewise for the control topology, in an overlay of n nodes, Kudos form approximately n1/2 clusters each containing n1/2 nodes. The merits of the hierarchy lies in its superior scaleability and low management overhead since measure probes are run across smaller groups and effects of member ...
Here, we present an approach that can quantify the interaction structure of signed digraphs and we define a node centrality measure for these networks. The basic principle behind our approach is to determine the sign and strength of direct and indirect effects of one node on another along ...
From this point of view, AFP can be modeled as a set of binary classifica- tion problems on graphs (one for every function), where nodes represent proteins, edges encode their functional similarity, and nodes are labeled according to the current function (see Fig. 1). Two main ...
Wu et al.11 advanced a technique capable of dynamically predicting the similarity of future node pairs, and calculated the similarity measure for nodes via an algorithm based on node ranking. Zhang et al.12 proposed a method integrating node centrality with time series to appraise the impact of...
complex, can be represented as a network. Importantly, such a network can be fruitfully studied to understand the system’s function. The discipline we use to study network systems is known as network science, a field that provides the necessary tools to represent, measure, and model networks[...