44,804 research outputs found
Handling oversampling in dynamic networks using link prediction
Oversampling is a common characteristic of data representing dynamic
networks. It introduces noise into representations of dynamic networks, but
there has been little work so far to compensate for it. Oversampling can affect
the quality of many important algorithmic problems on dynamic networks,
including link prediction. Link prediction seeks to predict edges that will be
added to the network given previous snapshots. We show that not only does
oversampling affect the quality of link prediction, but that we can use link
prediction to recover from the effects of oversampling. We also introduce a
novel generative model of noise in dynamic networks that represents
oversampling. We demonstrate the results of our approach on both synthetic and
real-world data.Comment: ECML/PKDD 201
Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach
The increasing availability of temporal network data is calling for more
research on extracting and characterizing mesoscopic structures in temporal
networks and on relating such structure to specific functions or properties of
the system. An outstanding challenge is the extension of the results achieved
for static networks to time-varying networks, where the topological structure
of the system and the temporal activity patterns of its components are
intertwined. Here we investigate the use of a latent factor decomposition
technique, non-negative tensor factorization, to extract the community-activity
structure of temporal networks. The method is intrinsically temporal and allows
to simultaneously identify communities and to track their activity over time.
We represent the time-varying adjacency matrix of a temporal network as a
three-way tensor and approximate this tensor as a sum of terms that can be
interpreted as communities of nodes with an associated activity time series. We
summarize known computational techniques for tensor decomposition and discuss
some quality metrics that can be used to tune the complexity of the factorized
representation. We subsequently apply tensor factorization to a temporal
network for which a ground truth is available for both the community structure
and the temporal activity patterns. The data we use describe the social
interactions of students in a school, the associations between students and
school classes, and the spatio-temporal trajectories of students over time. We
show that non-negative tensor factorization is capable of recovering the class
structure with high accuracy. In particular, the extracted tensor components
can be validated either as known school classes, or in terms of correlated
activity patterns, i.e., of spatial and temporal coincidences that are
determined by the known school activity schedule
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