429 research outputs found
Enumerating maximal cliques in link streams with durations
Link streams model interactions over time, and a clique in a link stream is
defined as a set of nodes and a time interval such that all pairs of nodes in
this set interact permanently during this time interval. This notion was
introduced recently in the case where interactions are instantaneous. We
generalize it to the case of interactions with durations and show that the
instantaneous case actually is a particular case of the case with durations. We
propose an algorithm to detect maximal cliques that improves our previous one
for instantaneous link streams, and performs better than the state of the art
algorithms in several cases of interest
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
Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks
In many domains, there is significant interest in capturing novel
relationships between time series that represent activities recorded at
different nodes of a highly complex system. In this paper, we introduce
multipoles, a novel class of linear relationships between more than two time
series. A multipole is a set of time series that have strong linear dependence
among themselves, with the requirement that each time series makes a
significant contribution to the linear dependence. We demonstrate that most
interesting multipoles can be identified as cliques of negative correlations in
a correlation network. Such cliques are typically rare in a real-world
correlation network, which allows us to find almost all multipoles efficiently
using a clique-enumeration approach. Using our proposed framework, we
demonstrate the utility of multipoles in discovering new physical phenomena in
two scientific domains: climate science and neuroscience. In particular, we
discovered several multipole relationships that are reproducible in multiple
other independent datasets and lead to novel domain insights.Comment: This is the accepted version of article submitted to IEEE
Transactions on Knowledge and Data Engineering 201
Enumerating Isolated Cliques in Temporal Networks
Isolation is a concept from the world of clique enumeration that is mostly
used to model communities that do not have much contact to the outside world.
Herein, a clique is considered isolated if it has few edges connecting it to
the rest of the graph. Motivated by recent work on enumerating cliques in
temporal networks, we lift the isolation concept to this setting. We discover
that the addition of the time dimension leads to six distinct natural isolation
concepts. Our main contribution is the development of fixed-parameter
enumeration algorithms for five of these six clique types employing the
parameter "degree of isolation". On the empirical side, we implement and test
these algorithms on (temporal) social network data, obtaining encouraging
preliminary results
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