18,140 research outputs found
Dynamical Properties of Interaction Data
Network dynamics are typically presented as a time series of network
properties captured at each period. The current approach examines the dynamical
properties of transmission via novel measures on an integrated, temporally
extended network representation of interaction data across time. Because it
encodes time and interactions as network connections, static network measures
can be applied to this "temporal web" to reveal features of the dynamics
themselves. Here we provide the technical details and apply it to agent-based
implementations of the well-known SEIR and SEIS epidemiological models.Comment: 29 pages, 15 figure
Correlation based networks of equity returns sampled at different time horizons
We investigate the planar maximally filtered graphs of the portfolio of the
300 most capitalized stocks traded at the New York Stock Exchange during the
time period 2001-2003. Topological properties such as the average length of
shortest paths, the betweenness and the degree are computed on different planar
maximally filtered graphs generated by sampling the returns at different time
horizons ranging from 5 min up to one trading day. This analysis confirms that
the selected stocks compose a hierarchical system progressively structuring as
the sampling time horizon increases. Finally, a cluster formation, associated
to economic sectors, is quantitatively investigated.Comment: 9 pages, 8 figure
Detecting and Refactoring Operational Smells within the Domain Name System
The Domain Name System (DNS) is one of the most important components of the
Internet infrastructure. DNS relies on a delegation-based architecture, where
resolution of names to their IP addresses requires resolving the names of the
servers responsible for those names. The recursive structures of the inter
dependencies that exist between name servers associated with each zone are
called dependency graphs. System administrators' operational decisions have far
reaching effects on the DNSs qualities. They need to be soundly made to create
a balance between the availability, security and resilience of the system. We
utilize dependency graphs to identify, detect and catalogue operational bad
smells. Our method deals with smells on a high-level of abstraction using a
consistent taxonomy and reusable vocabulary, defined by a DNS Operational
Model. The method will be used to build a diagnostic advisory tool that will
detect configuration changes that might decrease the robustness or security
posture of domain names before they become into production.Comment: In Proceedings GaM 2015, arXiv:1504.0244
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