Distributed Online Social Networks (DOSN) are a valid alternative to OSN based on peer-to-peer communications. Without centralised data management, DOSN must provide the users with higher level of control over their personal information and privacy. Thus, users may wish to restrict their personal network to a limited set of peers, depending on the level of trust with them. This means that the effective social network (used for information exchange) may be a subset of the complete social network, and may present different structural patterns, which could limit information diffusion. In this paper, we estimate the capability of DOSN to diffuse content based on trust between social peers. To have a realistic representation of a OSN friendship graph, we consider a large-scale Facebook network, from which we estimate the trust level between friends. Then, we consider only social links above a certain threshold of trust, and we analyse the potential capability of the resulting graph to spread information through several structural indices. We test four possible thresholds, coinciding with the definition of personal social circles derived from sociology and anthropology. The results show that limiting the network to "active social contacts" leads to a graph with high network connectivity, where the nodes are still wellconnected to each other, thus information can potentially cover a large number of nodes with respect to the original graph. On the other hand, the coverage drops for more restrictive assumptions. Nevertheless the re-insertion of a single excluded friend for each user is sufficient to obtain good coverage (i.e., always higher than 40 %) even in the most restricted graphs. We also analyse the potential capability of the network to spread information (i.e., network spreadability), studying the properties of the social paths between any pairs of users in the graph, which represent the effective channels traversed by information. The value of contact frequency between pairs of users determines a decay of trust along the path (the higher the contact frequency the lower the decay), and a consequent decay in the level of trustworthiness of information traversing the path. We show that selecting the link to re-insert in the network with probability proportional to its level of trust is the best re-insertion strategy, as it leads to the best connectivity/spreadability combination.
The vast proliferation of Online Social Networks (OSN) is generating many new ways to interact and create social relationships with others. While substantial results have been obtained in anthropology literature describing the properties of human social networks, a clear understanding of the properties of social networks built using OSN is still to be achieved. The presence of a huge amount of records containing users' communication history, provided by OSN, represents a new opportunity to analyse and better understand the human social behaviour. In this paper we present egonet digger, a novel Facebook application for the analysis of ego networks in OSN. Ego-net digger collects users' social data and gives a representation of their personal social networks according to the Dunbar's circles model. To show the potential of our application we analyse a sample data set collected during a data acquisition campaign, finding interesting similarities between the structure of Facebook ego networks and the properties found in the anthropology literature. Specifically, we find that, in our sample, there is a clear evidence of the presence of the same ego network structure -i.e., the Dunbar's circles -as found in human social networks formed offline.
The advent of Online Social Networks allowed scientists to remarkably improve the knowledge of the mechanisms controlling the formation of information diffusion chains in social networks (typically referred to as information cascades). Nevertheless, we are still not fully able to explain the role of social tie strength in the process, since most of the analyses in literature are based on unweighted networks only, i.e., they do not consider the social strength of the links between nodes. In this paper we contribute to fill this gap by analysing the properties of synthetically generated information cascades in a social network derived from a Facebook communication dataset. The properties of the cascades are studied in detail in relation to the characteristics of the nodes from which the diffusion starts (seed nodes). We compute, for both weighted and unweighted networks, the correlation between different measures of connectivity and centrality of the seed nodes, and the coverage of the resulting cascades. The results indicate that the knowledge of the strength of the social links is fundamental to infer which nodes will give rise to large information cascades.
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