8,129 research outputs found
Laplacian Change Point Detection for Dynamic Graphs
Dynamic and temporal graphs are rich data structures that are used to model
complex relationships between entities over time. In particular, anomaly
detection in temporal graphs is crucial for many real world applications such
as intrusion identification in network systems, detection of ecosystem
disturbances and detection of epidemic outbreaks. In this paper, we focus on
change point detection in dynamic graphs and address two main challenges
associated with this problem: I) how to compare graph snapshots across time,
II) how to capture temporal dependencies. To solve the above challenges, we
propose Laplacian Anomaly Detection (LAD) which uses the spectrum of the
Laplacian matrix of the graph structure at each snapshot to obtain low
dimensional embeddings. LAD explicitly models short term and long term
dependencies by applying two sliding windows. In synthetic experiments, LAD
outperforms the state-of-the-art method. We also evaluate our method on three
real dynamic networks: UCI message network, US senate co-sponsorship network
and Canadian bill voting network. In all three datasets, we demonstrate that
our method can more effectively identify anomalous time points according to
significant real world events.Comment: in KDD 2020, 10 page
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
Practical Attacks Against Graph-based Clustering
Graph modeling allows numerous security problems to be tackled in a general
way, however, little work has been done to understand their ability to
withstand adversarial attacks. We design and evaluate two novel graph attacks
against a state-of-the-art network-level, graph-based detection system. Our
work highlights areas in adversarial machine learning that have not yet been
addressed, specifically: graph-based clustering techniques, and a global
feature space where realistic attackers without perfect knowledge must be
accounted for (by the defenders) in order to be practical. Even though less
informed attackers can evade graph clustering with low cost, we show that some
practical defenses are possible.Comment: ACM CCS 201
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