31 research outputs found
Improving the robustness of online social networks: A simulation approach of network interventions
Online social networks (OSN) are prime examples of socio-technical systems in
which individuals interact via a technical platform. OSN are very volatile
because users enter and exit and frequently change their interactions. This
makes the robustness of such systems difficult to measure and to control. To
quantify robustness, we propose a coreness value obtained from the directed
interaction network. We study the emergence of large drop-out cascades of users
leaving the OSN by means of an agent-based model. For agents, we define a
utility function that depends on their relative reputation and their costs for
interactions. The decision of agents to leave the OSN depends on this utility.
Our aim is to prevent drop-out cascades by influencing specific agents with low
utility. We identify strategies to control agents in the core and the periphery
of the OSN such that drop-out cascades are significantly reduced, and the
robustness of the OSN is increased.Comment: 20 pages, 6 figure
Quantifying Triadic Closure in Multi-Edge Social Networks
Multi-edge networks capture repeated interactions between individuals. In
social networks, such edges often form closed triangles, or triads. Standard
approaches to measure this triadic closure, however, fail for multi-edge
networks, because they do not consider that triads can be formed by edges of
different multiplicity. We propose a novel measure of triadic closure for
multi-edge networks of social interactions based on a shared partner statistic.
We demonstrate that our operalization is able to detect meaningful closure in
synthetic and empirical multi-edge networks, where common approaches fail. This
is a cornerstone in driving inferential network analyses from the analysis of
binary networks towards the analyses of multi-edge and weighted networks, which
offer a more realistic representation of social interactions and relations.Comment: 19 pages, 5 figures, 6 table
HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks
The unsupervised detection of anomalies in time series data has important
applications in user behavioral modeling, fraud detection, and cybersecurity.
Anomaly detection has, in fact, been extensively studied in categorical
sequences. However, we often have access to time series data that represent
paths through networks. Examples include transaction sequences in financial
networks, click streams of users in networks of cross-referenced documents, or
travel itineraries in transportation networks. To reliably detect anomalies, we
must account for the fact that such data contain a large number of independent
observations of paths constrained by a graph topology. Moreover, the
heterogeneity of real systems rules out frequency-based anomaly detection
techniques, which do not account for highly skewed edge and degree statistics.
To address this problem, we introduce HYPA, a novel framework for the
unsupervised detection of anomalies in large corpora of variable-length
temporal paths in a graph. HYPA provides an efficient analytical method to
detect paths with anomalous frequencies that result from nodes being traversed
in unexpected chronological order.Comment: 11 pages with 8 figures and supplementary material. To appear at SIAM
Data Mining (SDM 2020
Predicting Sequences of Traversed Nodes in Graphs using Network Models with Multiple Higher Orders
We propose a novel sequence prediction method for sequential data capturing
node traversals in graphs. Our method builds on a statistical modelling
framework that combines multiple higher-order network models into a single
multi-order model. We develop a technique to fit such multi-order models in
empirical sequential data and to select the optimal maximum order. Our
framework facilitates both next-element and full sequence prediction given a
sequence-prefix of any length. We evaluate our model based on six empirical
data sets containing sequences from website navigation as well as public
transport systems. The results show that our method out-performs
state-of-the-art algorithms for next-element prediction. We further demonstrate
the accuracy of our method during out-of-sample sequence prediction and
validate that our method can scale to data sets with millions of sequences.Comment: 18 pages, 5 figures, 2 table
Reconstructing signed relations from interaction data
Positive and negative relations play an essential role in human behavior and
shape the communities we live in. Despite their importance, data about signed
relations is rare and commonly gathered through surveys. Interaction data is
more abundant, for instance, in the form of proximity or communication data. So
far, though, it could not be utilized to detect signed relations. In this
paper, we show how the underlying signed relations can be extracted with such
data. Employing a statistical network approach, we construct networks of signed
relations in four communities. We then show that these relations correspond to
the ones reported in surveys. Additionally, the inferred relations allow us to
study the homophily of individuals with respect to gender, religious beliefs,
and financial backgrounds. We evaluate the importance of triads in the signed
network to study group cohesion.Comment: 14 pages, 3 figures, submitte