18,867 research outputs found
Common adversaries form alliances: modelling complex networks via anti-transitivity
Anti-transitivity captures the notion that enemies of enemies are friends,
and arises naturally in the study of adversaries in social networks and in the
study of conflicting nation states or organizations. We present a simplified,
evolutionary model for anti-transitivity influencing link formation in complex
networks, and analyze the model's network dynamics. The Iterated Local
Anti-Transitivity (or ILAT) model creates anti-clone nodes in each time-step,
and joins anti-clones to the parent node's non-neighbor set. The graphs
generated by ILAT exhibit familiar properties of complex networks such as
densification, short distances (bounded by absolute constants), and bad
spectral expansion. We determine the cop and domination number for graphs
generated by ILAT, and finish with an analysis of their clustering
coefficients. We interpret these results within the context of real-world
complex networks and present open problems
Clones in Graphs
Finding structural similarities in graph data, like social networks, is a
far-ranging task in data mining and knowledge discovery. A (conceptually)
simple reduction would be to compute the automorphism group of a graph.
However, this approach is ineffective in data mining since real world data does
not exhibit enough structural regularity. Here we step in with a novel approach
based on mappings that preserve the maximal cliques. For this we exploit the
well known correspondence between bipartite graphs and the data structure
formal context from Formal Concept Analysis. From there we utilize
the notion of clone items. The investigation of these is still an open problem
to which we add new insights with this work. Furthermore, we produce a
substantial experimental investigation of real world data. We conclude with
demonstrating the generalization of clone items to permutations.Comment: 11 pages, 2 figures, 1 tabl
The Untold Story of the Clones: Content-agnostic Factors that Impact YouTube Video Popularity
Video dissemination through sites such as YouTube can have widespread impacts
on opinions, thoughts, and cultures. Not all videos will reach the same
popularity and have the same impact. Popularity differences arise not only
because of differences in video content, but also because of other
"content-agnostic" factors. The latter factors are of considerable interest but
it has been difficult to accurately study them. For example, videos uploaded by
users with large social networks may tend to be more popular because they tend
to have more interesting content, not because social network size has a
substantial direct impact on popularity. In this paper, we develop and apply a
methodology that is able to accurately assess, both qualitatively and
quantitatively, the impacts of various content-agnostic factors on video
popularity. When controlling for video content, we observe a strong linear
"rich-get-richer" behavior, with the total number of previous views as the most
important factor except for very young videos. The second most important factor
is found to be video age. We analyze a number of phenomena that may contribute
to rich-get-richer, including the first-mover advantage, and search bias
towards popular videos. For young videos we find that factors other than the
total number of previous views, such as uploader characteristics and number of
keywords, become relatively more important. Our findings also confirm that
inaccurate conclusions can be reached when not controlling for content.Comment: Dataset available at: http://www.ida.liu.se/~nikca/papers/kdd12.htm
Clone size distributions in networks of genetic similarity
We build networks of genetic similarity in which the nodes are organisms
sampled from biological populations. The procedure is illustrated by
constructing networks from genetic data of a marine clonal plant. An important
feature in the networks is the presence of clone subgraphs, i.e. sets of
organisms with identical genotype forming clones. As a first step to understand
the dynamics that has shaped these networks, we point up a relationship between
a particular degree distribution and the clone size distribution in the
populations. We construct a dynamical model for the population dynamics,
focussing on the dynamics of the clones, and solve it for the required
distributions. Scale free and exponentially decaying forms are obtained
depending on parameter values, the first type being obtained when clonal growth
is the dominant process. Average distributions are dominated by the power law
behavior presented by the fastest replicating populations.Comment: 17 pages, 4 figures. One figure improved and other minor changes. To
appear in Physica
Anergy in self-directed B lymphocytes from a statistical mechanics perspective
The ability of the adaptive immune system to discriminate between self and
non-self mainly stems from the ontogenic clonal-deletion of lymphocytes
expressing strong binding affinity with self-peptides. However, some
self-directed lymphocytes may evade selection and still be harmless due to a
mechanism called clonal anergy. As for B lymphocytes, two major explanations
for anergy developed over three decades: according to "Varela theory", it stems
from a proper orchestration of the whole B-repertoire, in such a way that
self-reactive clones, due to intensive interactions and feed-back from other
clones, display more inertia to mount a response. On the other hand, according
to the `two-signal model", which has prevailed nowadays, self-reacting cells
are not stimulated by helper lymphocytes and the absence of such signaling
yields anergy. The first result we present, achieved through disordered
statistical mechanics, shows that helper cells do not prompt the activation and
proliferation of a certain sub-group of B cells, which turn out to be just
those broadly interacting, hence it merges the two approaches as a whole (in
particular, Varela theory is then contained into the two-signal model). As a
second result, we outline a minimal topological architecture for the B-world,
where highly connected clones are self-directed as a natural consequence of an
ontogenetic learning; this provides a mathematical framework to Varela
perspective. As a consequence of these two achievements, clonal deletion and
clonal anergy can be seen as two inter-playing aspects of the same phenomenon
too
Random graph ensembles with many short loops
Networks observed in the real world often have many short loops. This
violates the tree-like assumption that underpins the majority of random graph
models and most of the methods used for their analysis. In this paper we sketch
possible research routes to be explored in order to make progress on networks
with many short loops, involving old and new random graph models and ideas for
novel mathematical methods. We do not present conclusive solutions of problems,
but aim to encourage and stimulate new activity and in what we believe to be an
important but under-exposed area of research. We discuss in more detail the
Strauss model, which can be seen as the `harmonic oscillator' of `loopy' random
graphs, and a recent exactly solvable immunological model that involves random
graphs with extensively many cliques and short loops.Comment: 18 pages, 10 figures,Mathematical Modelling of Complex Systems (Paris
2013) conferenc
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