2,282 research outputs found
Bounds for identifying codes in terms of degree parameters
An identifying code is a subset of vertices of a graph such that each vertex
is uniquely determined by its neighbourhood within the identifying code. If
\M(G) denotes the minimum size of an identifying code of a graph , it was
conjectured by F. Foucaud, R. Klasing, A. Kosowski and A. Raspaud that there
exists a constant such that if a connected graph with vertices and
maximum degree admits an identifying code, then \M(G)\leq
n-\tfrac{n}{d}+c. We use probabilistic tools to show that for any ,
\M(G)\leq n-\tfrac{n}{\Theta(d)} holds for a large class of graphs
containing, among others, all regular graphs and all graphs of bounded clique
number. This settles the conjecture (up to constants) for these classes of
graphs. In the general case, we prove \M(G)\leq n-\tfrac{n}{\Theta(d^{3})}.
In a second part, we prove that in any graph of minimum degree and
girth at least 5, \M(G)\leq(1+o_\delta(1))\tfrac{3\log\delta}{2\delta}n.
Using the former result, we give sharp estimates for the size of the minimum
identifying code of random -regular graphs, which is about
Topics in social network analysis and network science
This chapter introduces statistical methods used in the analysis of social
networks and in the rapidly evolving parallel-field of network science.
Although several instances of social network analysis in health services
research have appeared recently, the majority involve only the most basic
methods and thus scratch the surface of what might be accomplished.
Cutting-edge methods using relevant examples and illustrations in health
services research are provided
Synthetic Generation of Social Network Data With Endorsements
In many simulation studies involving networks there is the need to rely on a
sample network to perform the simulation experiments. In many cases, real
network data is not available due to privacy concerns. In that case we can
recourse to synthetic data sets with similar properties to the real data. In
this paper we discuss the problem of generating synthetic data sets for a
certain kind of online social network, for simulation purposes. Some popular
online social networks, such as LinkedIn and ResearchGate, allow user
endorsements for specific skills. For each particular skill, the endorsements
give rise to a directed subgraph of the corresponding network, where the nodes
correspond to network members or users, and the arcs represent endorsement
relations. Modelling these endorsement digraphs can be done by formulating an
optimization problem, which is amenable to different heuristics. Our
construction method consists of two stages: The first one simulates the growth
of the network, and the second one solves the aforementioned optimization
problem to construct the endorsements.Comment: 5 figures, 2 algorithms, Journal of Simulation 201
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