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Andrea Capocci

Publications and source records attributed to Andrea Capocci.

14 recordsLinked to original sources
arXiv2014

Degree correlations in signed social networks

We investigate degree correlations in two online social networks where users are connected through different types of links. We find that, while subnetworks in which links have a positive connotation, such as endorsement and trust, are characterized by assortative mixing by degree, networks in which links have a negative connotation, such as disapproval and distrust, are characterized by disassortative patterns. We introduce a class of simple theoretical models to analyze the interplay between network topology and the superimposed structure based on the sign of links. Results uncover the conditions that underpin the emergence of the patterns observed in the data, namely the assortativity of positive subnetworks and the disassortativity of negative ones. We discuss the implications of our study for the analysis of signed complex networks.

physics.soc-ph↗
arXiv2012

Statistical Properties of Inter-arrival Times Distribution in Social Tagging Systems

Folksonomies provide a rich source of data to study social patterns taking place on the World Wide Web. Here we study the temporal patterns of users' tagging activity. We show that the statistical properties of inter-arrival times between subsequent tagging events cannot be explained without taking into account correlation in users' behaviors. This shows that social interaction in collaborative tagging communities shapes the evolution of folksonomies. A consensus formation process involving the usage of a small number of tags for a given resources is observed through a numerical and analytical analysis of some well-known folksonomy datasets.

physics.soc-ph↗
arXiv2008

Self-organized network evolution coupled to extremal dynamics

The interplay between topology and dynamics in complex networks is a fundamental but widely unexplored problem. Here, we study this phenomenon on a prototype model in which the network is shaped by a dynamical variable. We couple the dynamics of the Bak-Sneppen evolution model with the rules of the so-called fitness network model for establishing the topology of a network; each vertex is assigned a fitness, and the vertex with minimum fitness and its neighbours are updated in each iteration. At the same time, the links between the updated vertices and all other vertices are drawn anew with a fitness-dependent connection probability. We show analytically and numerically that the system self-organizes to a non-trivial state that differs from what is obtained when the two processes are decoupled. A power-law decay of dynamical and topological quantities above a threshold emerges spontaneously, as well as a feedback between different dynamical regimes and the underlying correlation and percolation properties of the network.

cond-mat.stat-mech↗
arXiv2008

A Self-organized model for network evolution

Here we provide a detailed analysis, along with some extensions and additonal investigations, of a recently proposed self-organised model for the evolution of complex networks. Vertices of the network are characterised by a fitness variable evolving through an extremal dynamics process, as in the Bak-Sneppen model representing a prototype of Self-Organized Criticality. The network topology is in turn shaped by the fitness variable itself, as in the fitness network model. The system self-organizes to a nontrivial state, characterized by a power-law decay of dynamical and topological quantities above a critical threshold. The interplay between topology and dynamics in the system is the key ingredient leading to an unexpected behaviour of these quantities.

physics.soc-ph↗
arXiv2007

Folksonomies and clustering in the collaborative system CiteULike

We analyze CiteULike, an online collaborative tagging system where users bookmark and annotate scientific papers. Such a system can be naturally represented as a tripartite graph whose nodes represent papers, users and tags connected by individual tag assignments. The semantics of tags is studied here, in order to uncover the hidden relationships between tags. We find that the clustering coefficient reflects the semantical patterns among tags, providing useful ideas for the designing of more efficient methods of data classification and spam detection.

physics.soc-ph↗
arXiv2005

Loops structure of the Internet at the Autonomous System Level

We present here a study of the clustering and cycles in the graph of Internet at the Autonomous Systems level. We show that,even if the whole structure is changing with time, the statistical distributions of loops of order 3,4,5 remain stable during the evolution. Moreover we will bring evidence that the Internet graphs show characteristic Markovian signatures, since the loops structure is very well described by the two point correlations between the connectivities of the nodes. This represent another essential characteristic of this evolving graph. To capture this feature of the Internet represent one of the challenges in the future Internet modeling.

cond-mat.dis-nn↗
arXiv2005

Mixing properties of growing networks and the Simpson's paradox

We analyze the mixing properties of growing networks and find that, in some cases, the assortativity patterns are reversed once links' direction is considered: the disassortative behavior observed in such networks is a spurious effect, and a careful analysis reveals genuine positive correlations. We prove our claim by analytical calculations and numerical simulations for two classes of models based on preferential attachment and fitness. Such counterintuitive phenomenon is a manifestation of the well known Simpson's paradox. Results concerning mixing patterns may have important consequences, since they reflect on structural properties as resilience, epidemic spreading and synchronization. Our findings suggest that a more detailed analysis of real directed networks, such as the World Wide Web, is needed.

cond-mat.stat-mech↗
arXiv2005

Spectral methods cluster words of the same class in a syntactic dependency network

We analyze here a particular kind of linguistic network where vertices representwords and edges stand for syntactic relationships between words. The statisticalproperties of these networks have been recently studied and various features such as the small-world phenomenon and a scale-free distribution of degrees have been found. Our work focuses on four classes of words: verbs, nouns, adverbs and adjectives. Here, we use spectral methods sorting vertices. We show that the ordering clusters words of the same class. For nouns and verbs, the cluster size distribution clearly follows a power-law distribution that cannot be explained by a null hypothesis. Long-range correlations are found between vertices in theordering provided by the spectral method. The findings support the use of spectral methods for detecting community structure.

cond-mat.stat-mech↗
arXiv2004

Detecting communities in large networks

We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and links orientations. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable to the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words and in uncovering mental association patterns.

cond-mat.dis-nn↗
arXiv2003

Number of h-cycles in the Internet at the Autonomous Systems level

We present here a study of the clustering and cycles present in the graph of Internet at the Autonomous Systems level. Even if the whole structure is changing with time, we present some evidence that the statistical distributions of cycles of order 3,4,5 remain stable during the evolution. This could suggest that cycles are among the characteristic motifs of the Internet. Furthermore, we compare data with the results obtained for growing network models aimed to reproduce the Internet evolution. Namely the fitness model and the Generalized Network Growth model. We are able to find some qualitative agreement with the experimental situation even if the actual number of loops seems to be larger in the data than in any proposed growing network models. The task to capture this feature of the Internet represent one of the challenges in the future Internet modeling.

cond-mat.dis-nn↗
arXiv2002

Number of loops of size h in growing scale-free networks

The hierarchical structure of scale-free networks has been investigated focusing on the scaling of the number $N_h(t)$ of loops of size h as a function of the system size. In particular we have found the analytic expression for the scaling of $N_h(t)$ in the Barabási-Albert (BA) scale-free network. We have performed numerical simulations on the scaling law for $N_h(t)$ in the BA network and in other growing scale free networks, such as the bosonic network (BN) and the aging nodes (AN) network. We show that in the bosonic network and in the aging node network the phase transitions in the topology of the network are accompained by a change in the scaling of the number of loops with the system size.

cond-mat.dis-nn↗
arXiv2001

Market ecology of active and passive investors

We study the role of active and passive investors in an investment market with uncertainties. Active investors concentrate on a single or a few stocks with a given probability of determining the quality of them. Passive investors spread their investment uniformly, resembling buying the market index. In this toy market stocks are introduced as good and bad. If a stock receives sufficient investment it will survive, otherwise die. Active players exert a selective pressure since they can determine to an extent the investment quality. We show that the active players provide the driving force whereas the passive ones act as free riders. While their gains do not differ too much, we show that the active players enjoy an edge. Their presence also provides better gains to the passive players and stocks themselves.

cond-mat.dis-nn↗
arXiv1999

Driving Force in Investment

We study investment strategy in different models of financial markets, where the investors cannot reach a perfect knowledge about available assets. The investor spends a certain effort to get information; this allows him to better choose the investment strategy, and puts a selective pressure upon assets. The best strategy is then a compromise between diversification and effort to get information.

cond-mat.dis-nn↗