Narrow Results

Gossip monoids form an algebraic model of networks with exclusive, transient connections in which nodes, when they form a connection, exchange all known information. They also arise naturally in pure mathematics, as the monoids generated by the set of all equivalence relations on a given finite set under relational composition. We prove that a number of important decision problems for these monoids (including the membership problem, and hence the problem of deciding whether a given state of knowledge can arise in a network of the kind under consideration) are NP-complete. As well as being of interest in their own right, these results shed light on the apparent difficulty of establishing the cardinalities of the gossip monoids: a problem which has attracted some attention in the last few years.

Despite the increasing diffusion of the Internet technology, TV remains the principal medium of communication. People's perceptions, knowledge, beliefs and opinions about matters of fact get (in)formed through the information reported on by the media.

However, a single source of information (and consensus) could be a potential cause of anomalies in the structure and evolution of a society.

Hence, as the information available (and the way it is reported) is fundamental for our perceptions and opinions, the definition of conditions allowing for a good information to be disseminated is a pressing challenge. In this paper starting from a report on the last Italian political campaign in 2008, we derive a socio-cognitive computational model of opinion dynamics where agents get informed by different sources of information. Then, a what-if analysis, performed through simulations on the model's parameters space, is shown. In particular, the scenario implemented includes three main streams of information acquisition, differing in both the contents and the perceived reliability of the messages spread. Agents' internal opinion is updated either by accessing one of the information sources, namely media and experts, or by exchanging information with one another. They are also endowed with cognitive mechanisms to accept, reject or partially consider the acquired information.

In this work, we analyze gossip spreading on weighted networks. We try to define a new metric to classify weighted complex networks using our model. The model proposed here is based on the gossip spreading model introduced by Lind et al. on unweighted networks. The new metric is based on gossip spreading activity in the network, which is correlated with both topology and relative edge weights in the network. The model gives more insight about the weight distribution and correlation of topology with edge weights in a network. It also measures how suitable a weighted network is for gossip spreading. We analyze gossip spreading on real weighted networks of human interactions. Six co-occurrence and seven social pattern networks are investigated. Gossip propagation is found to be a good parameter to distinguish co-occurrence and social pattern networks. As a comparison some miscellaneous networks of comparable sizes and computer generated networks based on ER, BA and WS models are also investigated. They are found to be quite different from the human interaction networks.