862,413 research outputs found
Discovering New Sentiments from the Social Web
A persistent challenge in Complex Systems (CS) research is the
phenomenological reconstruction of systems from raw data. In order to face the
problem, the use of sound features to reason on the system from data processing
is a key step. In the specific case of complex societal systems, sentiment
analysis allows to mirror (part of) the affective dimension. However it is not
reasonable to think that individual sentiment categorization can encompass the
new affective phenomena in digital social networks.
The present papers addresses the problem of isolating sentiment concepts
which emerge in social networks. In an analogy to Artificial Intelligent
Singularity, we propose the study and analysis of these new complex sentiment
structures and how they are similar to or diverge from classic conceptual
structures associated to sentiment lexicons. The conjecture is that it is
highly probable that hypercomplex sentiment structures -not explained with
human categorizations- emerge from high dynamic social information networks.
Roughly speaking, new sentiment can emerge from the new global nervous systems
as it occurs in humans
Self-similarity of complex networks
Complex networks have been studied extensively due to their relevance to many
real systems as diverse as the World-Wide-Web (WWW), the Internet, energy
landscapes, biological and social networks
\cite{ab-review,mendes,vespignani,newman,amaral}. A large number of real
networks are called ``scale-free'' because they show a power-law distribution
of the number of links per node \cite{ab-review,barabasi1999,faloutsos}.
However, it is widely believed that complex networks are not {\it length-scale}
invariant or self-similar. This conclusion originates from the ``small-world''
property of these networks, which implies that the number of nodes increases
exponentially with the ``diameter'' of the network
\cite{erdos,bollobas,milgram,watts}, rather than the power-law relation
expected for a self-similar structure. Nevertheless, here we present a novel
approach to the analysis of such networks, revealing that their structure is
indeed self-similar. This result is achieved by the application of a
renormalization procedure which coarse-grains the system into boxes containing
nodes within a given "size". Concurrently, we identify a power-law relation
between the number of boxes needed to cover the network and the size of the box
defining a finite self-similar exponent. These fundamental properties, which
are shown for the WWW, social, cellular and protein-protein interaction
networks, help to understand the emergence of the scale-free property in
complex networks. They suggest a common self-organization dynamics of diverse
networks at different scales into a critical state and in turn bring together
previously unrelated fields: the statistical physics of complex networks with
renormalization group, fractals and critical phenomena.Comment: 28 pages, 12 figures, more informations at http://www.jamlab.or
The Physics of Communicability in Complex Networks
A fundamental problem in the study of complex networks is to provide
quantitative measures of correlation and information flow between different
parts of a system. To this end, several notions of communicability have been
introduced and applied to a wide variety of real-world networks in recent
years. Several such communicability functions are reviewed in this paper. It is
emphasized that communication and correlation in networks can take place
through many more routes than the shortest paths, a fact that may not have been
sufficiently appreciated in previously proposed correlation measures. In
contrast to these, the communicability measures reviewed in this paper are
defined by taking into account all possible routes between two nodes, assigning
smaller weights to longer ones. This point of view naturally leads to the
definition of communicability in terms of matrix functions, such as the
exponential, resolvent, and hyperbolic functions, in which the matrix argument
is either the adjacency matrix or the graph Laplacian associated with the
network. Considerable insight on communicability can be gained by modeling a
network as a system of oscillators and deriving physical interpretations, both
classical and quantum-mechanical, of various communicability functions.
Applications of communicability measures to the analysis of complex systems are
illustrated on a variety of biological, physical and social networks. The last
part of the paper is devoted to a review of the notion of locality in complex
networks and to computational aspects that by exploiting sparsity can greatly
reduce the computational efforts for the calculation of communicability
functions for large networks.Comment: Review Article. 90 pages, 14 figures. Contents: Introduction;
Communicability in Networks; Physical Analogies; Comparing Communicability
Functions; Communicability and the Analysis of Networks; Communicability and
Localization in Complex Networks; Computability of Communicability Functions;
Conclusions and Prespective
Let Your CyberAlter Ego Share Information and Manage Spam
Almost all of us have multiple cyberspace identities, and these {\em
cyber}alter egos are networked together to form a vast cyberspace social
network. This network is distinct from the world-wide-web (WWW), which is being
queried and mined to the tune of billions of dollars everyday, and until
recently, has gone largely unexplored. Empirically, the cyberspace social
networks have been found to possess many of the same complex features that
characterize its real counterparts, including scale-free degree distributions,
low diameter, and extensive connectivity. We show that these topological
features make the latent networks particularly suitable for explorations and
management via local-only messaging protocols. {\em Cyber}alter egos can
communicate via their direct links (i.e., using only their own address books)
and set up a highly decentralized and scalable message passing network that can
allow large-scale sharing of information and data. As one particular example of
such collaborative systems, we provide a design of a spam filtering system, and
our large-scale simulations show that the system achieves a spam detection rate
close to 100%, while the false positive rate is kept around zero. This system
has several advantages over other recent proposals (i) It uses an already
existing network, created by the same social dynamics that govern our daily
lives, and no dedicated peer-to-peer (P2P) systems or centralized server-based
systems need be constructed; (ii) It utilizes a percolation search algorithm
that makes the query-generated traffic scalable; (iii) The network has a built
in trust system (just as in social networks) that can be used to thwart
malicious attacks; iv) It can be implemented right now as a plugin to popular
email programs, such as MS Outlook, Eudora, and Sendmail.Comment: 13 pages, 10 figure
Value Computations in Ventral Medial Prefrontal Cortex during Charitable Decision Making Incorporate Input from Regions Involved in Social Cognition
Little is known about the neural networks supporting value computation during complex social decisions. We investigated this question using functional magnetic resonance imaging while subjects made donations to different charities. We found that the blood oxygenation level-dependent signal in ventral medial prefrontal cortex (VMPFC) correlated with the subjective value of voluntary donations. Furthermore, the region of the VMPFC identified showed considerable overlap with regions that have been shown to encode for the value of basic rewards at the time of choice, suggesting that it might serve as a common valuation system during decision making. In addition, functional connectivity analyses indicated that the value signal in VMPFC might integrate inputs from networks, including the anterior insula and posterior superior temporal cortex, that are thought to be involved in social cognition
Model of mobile agents for sexual interactions networks
We present a novel model to simulate real social networks of complex
interactions, based in a granular system of colliding particles (agents). The
network is build by keeping track of the collisions and evolves in time with
correlations which emerge due to the mobility of the agents. Therefore,
statistical features are a consequence only of local collisions among its
individual agents. Agent dynamics is realized by an event-driven algorithm of
collisions where energy is gained as opposed to granular systems which have
dissipation. The model reproduces empirical data from networks of sexual
interactions, not previously obtained with other approaches.Comment: 6 pages, 8 figure
- …