25,065 research outputs found
A Unifying Model for Representing Time-Varying Graphs
Graph-based models form a fundamental aspect of data representation in Data
Sciences and play a key role in modeling complex networked systems. In
particular, recently there is an ever-increasing interest in modeling dynamic
complex networks, i.e. networks in which the topological structure (nodes and
edges) may vary over time. In this context, we propose a novel model for
representing finite discrete Time-Varying Graphs (TVGs), which are typically
used to model dynamic complex networked systems. We analyze the data structures
built from our proposed model and demonstrate that, for most practical cases,
the asymptotic memory complexity of our model is in the order of the
cardinality of the set of edges. Further, we show that our proposal is an
unifying model that can represent several previous (classes of) models for
dynamic networks found in the recent literature, which in general are unable to
represent each other. In contrast to previous models, our proposal is also able
to intrinsically model cyclic (i.e. periodic) behavior in dynamic networks.
These representation capabilities attest the expressive power of our proposed
unifying model for TVGs. We thus believe our unifying model for TVGs is a step
forward in the theoretical foundations for data analysis of complex networked
systems.Comment: Also appears in the Proc. of the IEEE International Conference on
Data Science and Advanced Analytics (IEEE DSAA'2015
A Unifying Model for Representing Time-Varying Graphs
We propose a novel model for representing finite discrete Time-Varying Graphs~(TVGs). We show how key concepts, such as degree, path, and connectivity, are handled in our model. We also analyze the data structures built following our proposed model and demonstrate that, for most practical cases, the asymptotic memory complexity of our model is restricted to the cardinality of the set of edges. Moreover, we prove that if the TVG nodes can be considered as independent entities at each time instant, the analyzed TVG is isomorphic to a directed static graph. This is an important theoretical result since this allows the use of the isomorphic directed graph as a tool to analyze both the properties of a TVG and the behavior of dynamic processes over a TVG. We also show that our unifying model can represent several previous (classes of) models for dynamic networks found in the recent literature, which in general are unable to represent each other. In contrast to previous models, our proposal is also able to intrinsically model cyclic~(i.e. periodic) behavior in dynamic networks. These representation capabilities attest the expressive power of our proposed unifying model for TVGs.Nous proposons un modèle (TVG pour \emph{Time-Varying Graphs}) pour représenter les graphes dynamiques (\emph{i.e.}, des graphes susceptibles d'évoluer au cours du temps). Nous montrons qu elles définitionsclefs comme le degré, la notion de chemin, de connectivité sont prise en compte par ce modèle. Une analyse de la complexité des structures de données nécessaire à la représentation de ce modèle montre que la complexité asymptotique est en (cardinalité du nombre d'arêtes du graphe dynamique). Si les sommets d'un TVG peuvent être considérés comme des entités indépendantes à chaque instant, alors on démontre que le graphe TVG est isomorphe à un graphe orienté static. Notre modèle permet de représenter et de prendre en compte les différentes propositions existantes qui n'étaient pas en mesure de se représenter les unes les autres
Time-Varying Graphs and Dynamic Networks
The past few years have seen intensive research efforts carried out in some
apparently unrelated areas of dynamic systems -- delay-tolerant networks,
opportunistic-mobility networks, social networks -- obtaining closely related
insights. Indeed, the concepts discovered in these investigations can be viewed
as parts of the same conceptual universe; and the formal models proposed so far
to express some specific concepts are components of a larger formal description
of this universe. The main contribution of this paper is to integrate the vast
collection of concepts, formalisms, and results found in the literature into a
unified framework, which we call TVG (for time-varying graphs). Using this
framework, it is possible to express directly in the same formalism not only
the concepts common to all those different areas, but also those specific to
each. Based on this definitional work, employing both existing results and
original observations, we present a hierarchical classification of TVGs; each
class corresponds to a significant property examined in the distributed
computing literature. We then examine how TVGs can be used to study the
evolution of network properties, and propose different techniques, depending on
whether the indicators for these properties are a-temporal (as in the majority
of existing studies) or temporal. Finally, we briefly discuss the introduction
of randomness in TVGs.Comment: A short version appeared in ADHOC-NOW'11. This version is to be
published in Internation Journal of Parallel, Emergent and Distributed
System
Discovering Patterns of Interest in IP Traffic Using Cliques in Bipartite Link Streams
Studying IP traffic is crucial for many applications. We focus here on the
detection of (structurally and temporally) dense sequences of interactions,
that may indicate botnets or coordinated network scans. More precisely, we
model a MAWI capture of IP traffic as a link streams, i.e. a sequence of
interactions meaning that devices and exchanged
packets from time to time . This traffic is captured on a single
router and so has a bipartite structure: links occur only between nodes in two
disjoint sets. We design a method for finding interesting bipartite cliques in
such link streams, i.e. two sets of nodes and a time interval such that all
nodes in the first set are linked to all nodes in the second set throughout the
time interval. We then explore the bipartite cliques present in the considered
trace. Comparison with the MAWILab classification of anomalous IP addresses
shows that the found cliques succeed in detecting anomalous network activity
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