21,731 research outputs found
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
Efficient Identification of Equivalences in Dynamic Graphs and Pedigree Structures
We propose a new framework for designing test and query functions for complex
structures that vary across a given parameter such as genetic marker position.
The operations we are interested in include equality testing, set operations,
isolating unique states, duplication counting, or finding equivalence classes
under identifiability constraints. A motivating application is locating
equivalence classes in identity-by-descent (IBD) graphs, graph structures in
pedigree analysis that change over genetic marker location. The nodes of these
graphs are unlabeled and identified only by their connecting edges, a
constraint easily handled by our approach. The general framework introduced is
powerful enough to build a range of testing functions for IBD graphs, dynamic
populations, and other structures using a minimal set of operations. The
theoretical and algorithmic properties of our approach are analyzed and proved.
Computational results on several simulations demonstrate the effectiveness of
our approach.Comment: Code for paper available at
http://www.stat.washington.edu/~hoytak/code/hashreduc
Challenges in Bridging Social Semantics and Formal Semantics on the Web
This paper describes several results of Wimmics, a research lab which names
stands for: web-instrumented man-machine interactions, communities, and
semantics. The approaches introduced here rely on graph-oriented knowledge
representation, reasoning and operationalization to model and support actors,
actions and interactions in web-based epistemic communities. The re-search
results are applied to support and foster interactions in online communities
and manage their resources
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