27,463 research outputs found
Why (and How) Networks Should Run Themselves
The proliferation of networked devices, systems, and applications that we
depend on every day makes managing networks more important than ever. The
increasing security, availability, and performance demands of these
applications suggest that these increasingly difficult network management
problems be solved in real time, across a complex web of interacting protocols
and systems. Alas, just as the importance of network management has increased,
the network has grown so complex that it is seemingly unmanageable. In this new
era, network management requires a fundamentally new approach. Instead of
optimizations based on closed-form analysis of individual protocols, network
operators need data-driven, machine-learning-based models of end-to-end and
application performance based on high-level policy goals and a holistic view of
the underlying components. Instead of anomaly detection algorithms that operate
on offline analysis of network traces, operators need classification and
detection algorithms that can make real-time, closed-loop decisions. Networks
should learn to drive themselves. This paper explores this concept, discussing
how we might attain this ambitious goal by more closely coupling measurement
with real-time control and by relying on learning for inference and prediction
about a networked application or system, as opposed to closed-form analysis of
individual protocols
Matchability of heterogeneous networks pairs
We consider the problem of graph matchability in non-identically distributed networks. In a general class of edge-independent networks, we demonstrate that graph matchability is almost surely lost when matching the networks directly, and is almost perfectly recovered when first centering the networks using Universal Singular Value Thresholding before matching. These theoretical results are then demonstrated in both real and synthetic simulation settings. We also recover analogous core-matchability results in a very general core-junk network model, wherein some vertices do not correspond between the graph pair.First author draf
On the Complexity of Exact Pattern Matching in Graphs: Binary Strings and Bounded Degree
Exact pattern matching in labeled graphs is the problem of searching paths of
a graph that spell the same string as the pattern . This
basic problem can be found at the heart of more complex operations on variation
graphs in computational biology, of query operations in graph databases, and of
analysis operations in heterogeneous networks, where the nodes of some paths
must match a sequence of labels or types. We describe a simple conditional
lower bound that, for any constant , an -time or an -time algorithm for exact pattern
matching on graphs, with node labels and patterns drawn from a binary alphabet,
cannot be achieved unless the Strong Exponential Time Hypothesis (SETH) is
false. The result holds even if restricted to undirected graphs of maximum
degree three or directed acyclic graphs of maximum sum of indegree and
outdegree three. Although a conditional lower bound of this kind can be somehow
derived from previous results (Backurs and Indyk, FOCS'16), we give a direct
reduction from SETH for dissemination purposes, as the result might interest
researchers from several areas, such as computational biology, graph database,
and graph mining, as mentioned before. Indeed, as approximate pattern matching
on graphs can be solved in time, exact and approximate matching are
thus equally hard (quadratic time) on graphs under the SETH assumption. In
comparison, the same problems restricted to strings have linear time vs
quadratic time solutions, respectively, where the latter ones have a matching
SETH lower bound on computing the edit distance of two strings (Backurs and
Indyk, STOC'15).Comment: Using Lemma 12 and Lemma 13 might to be enough to prove Lemma 14.
However, the proof of Lemma 14 is correct if you assume that the graph used
in the reduction is a DAG. Hence, since the problem is already quadratic for
a DAG and a binary alphabet, it has to be quadratic also for a general graph
and a binary alphabe
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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