2,193 research outputs found
A Network Model characterized by a Latent Attribute Structure with Competition
The quest for a model that is able to explain, describe, analyze and simulate
real-world complex networks is of uttermost practical as well as theoretical
interest. In this paper we introduce and study a network model that is based on
a latent attribute structure: each node is characterized by a number of
features and the probability of the existence of an edge between two nodes
depends on the features they share. Features are chosen according to a process
of Indian-Buffet type but with an additional random "fitness" parameter
attached to each node, that determines its ability to transmit its own features
to other nodes. As a consequence, a node's connectivity does not depend on its
age alone, so also "young" nodes are able to compete and succeed in acquiring
links. One of the advantages of our model for the latent bipartite
"node-attribute" network is that it depends on few parameters with a
straightforward interpretation. We provide some theoretical, as well
experimental, results regarding the power-law behaviour of the model and the
estimation of the parameters. By experimental data, we also show how the
proposed model for the attribute structure naturally captures most local and
global properties (e.g., degree distributions, connectivity and distance
distributions) real networks exhibit. keyword: Complex network, social network,
attribute matrix, Indian Buffet processComment: 34 pages, second version (date of the first version: July, 2014).
Submitte
Evaluating Community Detection Algorithms for Progressively Evolving Graphs
Many algorithms have been proposed in the last ten years for the discovery of
dynamic communities. However, these methods are seldom compared between
themselves. In this article, we propose a generator of dynamic graphs with
planted evolving community structure, as a benchmark to compare and evaluate
such algorithms. Unlike previously proposed benchmarks, it is able to specify
any desired evolving community structure through a descriptive language, and
then to generate the corresponding progressively evolving network. We
empirically evaluate six existing algorithms for dynamic community detection in
terms of instantaneous and longitudinal similarity with the planted ground
truth, smoothness of dynamic partitions, and scalability. We notably observe
different types of weaknesses depending on their approach to ensure smoothness,
namely Glitches, Oversimplification and Identity loss. Although no method
arises as a clear winner, we observe clear differences between methods, and we
identified the fastest, those yielding the most smoothed or the most accurate
solutions at each step
Logical limit laws for minor-closed classes of graphs
Let be an addable, minor-closed class of graphs. We prove that
the zero-one law holds in monadic second-order logic (MSO) for the random graph
drawn uniformly at random from all {\em connected} graphs in on
vertices, and the convergence law in MSO holds if we draw uniformly at
random from all graphs in on vertices. We also prove analogues
of these results for the class of graphs embeddable on a fixed surface,
provided we restrict attention to first order logic (FO). Moreover, the
limiting probability that a given FO sentence is satisfied is independent of
the surface . We also prove that the closure of the set of limiting
probabilities is always the finite union of at least two disjoint intervals,
and that it is the same for FO and MSO. For the classes of forests and planar
graphs we are able to determine the closure of the set of limiting
probabilities precisely. For planar graphs it consists of exactly 108
intervals, each of length . Finally, we analyse
examples of non-addable classes where the behaviour is quite different. For
instance, the zero-one law does not hold for the random caterpillar on
vertices, even in FO.Comment: minor changes; accepted for publication by JCT
Random Metric Spaces and Universality
WWe define the notion of a random metric space and prove that with
probability one such a space is isometricto the Urysohn universal metric space.
The main technique is the study of universal and random distance matrices; we
relate the properties of metric (in particulary universal) space to the
properties of distance matrices. We show the link between those questions and
classification of the Polish spaces with measure (Gromov or metric triples) and
with the problem about S_{\infty}-invariant measures in the space of symmetric
matrices. One of the new effects -exsitence in Urysohn space so called
anarchical uniformly distributed sequences. We give examples of other
categories in which the randomness and universality coincide (graph, etc.).Comment: 38 PAGE
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