Assessing Centrality Without Knowing Connections

We consider the privacy-preserving computation of node influence in distributed social networks, as measured by egocentric betweenness centrality (EBC). Motivated by modern communication networks spanning multiple providers, we show for the first time how multiple mutually-distrusting parties can successfully compute node EBC while revealing only differentially-private information about their internal network connections. A theoretical utility analysis upper bounds a primary source of private EBC error---private release of ego networks---with high probability. Empirical results demonstrate practical applicability with a low 1.07 relative error achievable at strong privacy budget $\epsilon=0.1$ on a Facebook graph, and insignificant performance degradation as the number of network provider parties grows.Comment: Full report of paper appearing in PAKDD202

Social networks and performance in distributed learning communities

Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this study we analyse two distributed learning communities' social networks in order to understand how characteristics of the social structure can enhance students' success and performance. We used a monitoring system for social network data gathering. Results from correlation analyses showed that students' social network characteristics are related to their performancePostprint (published version

The Safety Net as a Network

The lack of a coherent understanding of what is meant by the American safety net made it difficult to have a meaningful discourse on the current condition. This paper proposes an alternative formulation of the social safety net based in network theory to overcome the shortcomings of the previous literature. The first part of the paper describes this approach, attempting to develop an alternative understanding of the safety net grounded in the actions of anti-poverty actors. Next is a list of propositions for measuring five dimensions of a safety net: the frame, structure, positions, influences, and the context. Three policy implications are derived from this new paradigm. First, shifting the level of analysis to network level allows policy makers to broaden the scope of the modern social safety net. Second, quantifying the interaction among actors reveals interdependency, which in turn redefines the power and influence of each actor within the network. Finally, the modern safety net could demonstrate a core-periphery structure. It calls for a new way of thinking about resource distribution and decision making channels of such unique structure.LBJ School of Public Affair

Identifying well-connected opinion leaders for informal health promotion: the example of the ASSIST smoking prevention program

Methods used to select opinion leaders for informal behavior change interventions vary, affecting the role they adopt and the outcomes of interventions. The development of successful identification methods requires evidence that these methods achieve their aims. This study explored whether the “whole community” nomination process used in the ASSIST smoking prevention program successfully identified “peer supporters” who were well placed within their school social networks to diffuse an antismoking message to their peers. Data were collected in the United Kingdom during A Stop Smoking in Schools Trial. Behavioral data were provided at baseline and post intervention by all students. Social network data were provided post intervention by students in four control and six intervention schools. Centrality measures calculated using UCINET demonstrate that the ASSIST nomination process successfully identified peer supporters who were more socially connected than others in their year and who had social connections across the entire year group including the program’s target group. The results indicate that three simple questions can identify individuals who are held in high esteem by their year group and who also have the interpersonal networks required of opinion leaders to successfully disseminate smoke-free messages through their social networks. This approach could be used in other informal health promotion initiatives

Interbank markets and multiplex networks: centrality measures and statistical null models

The interbank market is considered one of the most important channels of contagion. Its network representation, where banks and claims/obligations are represented by nodes and links (respectively), has received a lot of attention in the recent theoretical and empirical literature, for assessing systemic risk and identifying systematically important financial institutions. Different types of links, for example in terms of maturity and collateralization of the claim/obligation, can be established between financial institutions. Therefore a natural representation of the interbank structure which takes into account more features of the market, is a multiplex, where each layer is associated with a type of link. In this paper we review the empirical structure of the multiplex and the theoretical consequences of this representation. We also investigate the betweenness and eigenvector centrality of a bank in the network, comparing its centrality properties across different layers and with Maximum Entropy null models.Comment: To appear in the book "Interconnected Networks", A. Garas e F. Schweitzer (eds.), Springer Complexity Serie

Prediction of lethal and synthetically lethal knock-outs in regulatory networks

The complex interactions involved in regulation of a cell's function are captured by its interaction graph. More often than not, detailed knowledge about enhancing or suppressive regulatory influences and cooperative effects is lacking and merely the presence or absence of directed interactions is known. Here we investigate to which extent such reduced information allows to forecast the effect of a knock-out or a combination of knock-outs. Specifically we ask in how far the lethality of eliminating nodes may be predicted by their network centrality, such as degree and betweenness, without knowing the function of the system. The function is taken as the ability to reproduce a fixed point under a discrete Boolean dynamics. We investigate two types of stochastically generated networks: fully random networks and structures grown with a mechanism of node duplication and subsequent divergence of interactions. On all networks we find that the out-degree is a good predictor of the lethality of a single node knock-out. For knock-outs of node pairs, the fraction of successors shared between the two knocked-out nodes (out-overlap) is a good predictor of synthetic lethality. Out-degree and out-overlap are locally defined and computationally simple centrality measures that provide a predictive power close to the optimal predictor.Comment: published version, 10 pages, 6 figures, 2 tables; supplement at http://www.bioinf.uni-leipzig.de/publications/supplements/11-01