Determining the critical nodes in a network given a certain network measure is a computational challenging problem that requires the design of adaptive and scalable algorithms. The number of connected components in a graph is an example of such a measure: in this case the nodes considered critical are those that, if removed from…
Critical node detection is a crucial task in network analysis. In this article a new problem is proposed, the critical node detection in hypergraphs, which are a generalization of the ‘traditional’ graphs. A genetic algorithm is proposed to solve this problem and as an application an inflation dataset is transformed in a hypergraph and…
An important problem in network science is finding relevant community structures in complex networks. A community structure is a partition of the network nodes into clusters or modules, such that each cluster is densely connected. Current community detection algorithms have time complexity, centralization, and scalability issues. In this paper, to solve this problem, we…
The stability and robustness of a complex network can be significantly improved by determining important nodes and by analyzing their tendency to group into clusters. Several centrality measures for evaluating the importance of a node in a complex network exist in the literature, each one focusing on a different perspective. Community detection algorithms can…
Abstract Due to the increasing number of new malware appearing daily, it is impossible to manually inspect each sample. By applying data mining techniques to analyze the program code, we can help manual pro- cessing. In this paper we propose a method to extract signatures from the executable binary of a malware, in order…
Complex Networks theory represents a powerful tool to model real-world systems as graphs with non-trivial topological features. Static by their definition, complex networks are limited to be the reflection or the snapshot of the dynamical systems they encode in a given moment. Frankly, studies show that the network preserves the characteristics of the dynamic…
Properties of complex networks represent a powerful set of tools that can be used to study the complex behaviour of these systems of interconnections. They can vary from properties represented as simplistic metrics (number of edges and nodes) to properties that reflect complex information of the connection between entities part of the network (assortativity…
Complex networks are data structures with great importance in representing real world interactions which surrounds us. While their structures might look chaotic at a first glance, the focus of most on-going studies in this field is in understanding how their topological properties influence the dynamics of a complex network’s structure in order to prove…
Complex networks are powerful mechanisms one can use to model real-world networks as topological spaces. The beauty of these structures is provided by the infinite degree of analysis one is allowed to do using them. Biologically it is almost impossible for the human mind to comprehend the behaviour of these systems, but when modelled…
Network motifs bring a great interest to many fields, because they are a perfect candidate to speed up the applied research in the understanding of complex networks dynamics. One of the biggest problems raised in the process of finding these motifs is the performance. Starting from splitting the initial network into subgraphs to the…