1,578 research outputs found
Router-level community structure of the Internet Autonomous Systems
The Internet is composed of routing devices connected between them and
organized into independent administrative entities: the Autonomous Systems. The
existence of different types of Autonomous Systems (like large connectivity
providers, Internet Service Providers or universities) together with
geographical and economical constraints, turns the Internet into a complex
modular and hierarchical network. This organization is reflected in many
properties of the Internet topology, like its high degree of clustering and its
robustness.
In this work, we study the modular structure of the Internet router-level
graph in order to assess to what extent the Autonomous Systems satisfy some of
the known notions of community structure. We show that the modular structure of
the Internet is much richer than what can be captured by the current community
detection methods, which are severely affected by resolution limits and by the
heterogeneity of the Autonomous Systems. Here we overcome this issue by using a
multiresolution detection algorithm combined with a small sample of nodes. We
also discuss recent work on community structure in the light of our results
Detecting Cohesive and 2-mode Communities in Directed and Undirected Networks
Networks are a general language for representing relational information among
objects. An effective way to model, reason about, and summarize networks, is to
discover sets of nodes with common connectivity patterns. Such sets are
commonly referred to as network communities. Research on network community
detection has predominantly focused on identifying communities of densely
connected nodes in undirected networks.
In this paper we develop a novel overlapping community detection method that
scales to networks of millions of nodes and edges and advances research along
two dimensions: the connectivity structure of communities, and the use of edge
directedness for community detection. First, we extend traditional definitions
of network communities by building on the observation that nodes can be densely
interlinked in two different ways: In cohesive communities nodes link to each
other, while in 2-mode communities nodes link in a bipartite fashion, where
links predominate between the two partitions rather than inside them. Our
method successfully detects both 2-mode as well as cohesive communities, that
may also overlap or be hierarchically nested. Second, while most existing
community detection methods treat directed edges as though they were
undirected, our method accounts for edge directions and is able to identify
novel and meaningful community structures in both directed and undirected
networks, using data from social, biological, and ecological domains.Comment: Published in the proceedings of WSDM '1
Differentially Private One Permutation Hashing and Bin-wise Consistent Weighted Sampling
Minwise hashing (MinHash) is a standard algorithm widely used in the
industry, for large-scale search and learning applications with the binary
(0/1) Jaccard similarity. One common use of MinHash is for processing massive
n-gram text representations so that practitioners do not have to materialize
the original data (which would be prohibitive). Another popular use of MinHash
is for building hash tables to enable sub-linear time approximate near neighbor
(ANN) search. MinHash has also been used as a tool for building large-scale
machine learning systems. The standard implementation of MinHash requires
applying random permutations. In comparison, the method of one permutation
hashing (OPH), is an efficient alternative of MinHash which splits the data
vectors into bins and generates hash values within each bin. OPH is
substantially more efficient and also more convenient to use.
In this paper, we combine the differential privacy (DP) with OPH (as well as
MinHash), to propose the DP-OPH framework with three variants: DP-OPH-fix,
DP-OPH-re and DP-OPH-rand, depending on which densification strategy is adopted
to deal with empty bins in OPH. A detailed roadmap to the algorithm design is
presented along with the privacy analysis. An analytical comparison of our
proposed DP-OPH methods with the DP minwise hashing (DP-MH) is provided to
justify the advantage of DP-OPH. Experiments on similarity search confirm the
merits of DP-OPH, and guide the choice of the proper variant in different
practical scenarios. Our technique is also extended to bin-wise consistent
weighted sampling (BCWS) to develop a new DP algorithm called DP-BCWS for
non-binary data. Experiments on classification tasks demonstrate that DP-BCWS
is able to achieve excellent utility at around , where
is the standard parameter in the language of -DP
4DGVF segmentation of vector-valued images
International audienceIn this paper, we extend the gradient vector flow field to the vector-valued case for robust variational segmentation of 4D images with active surfaces. Instead of only exploiting scalar edge strength in order to identify vector edges, we propagate both directions and amplitudes of vector gradients computed from the analysis of a structure tensor of the vector-valued image. To reduce contributions from noise in the calculation of the structure tensor, image channels are weighted according to a blind estimator of contrast that take profit of the deformable models framework. The proposed 4DGVF vector field is validated on synthetic image datasets and applied to biological volume delineation in dynamic PET imaging
Comparing Community Structure to Characteristics in Online Collegiate Social Networks
We study the structure of social networks of students by examining the graphs
of Facebook "friendships" at five American universities at a single point in
time. We investigate each single-institution network's community structure and
employ graphical and quantitative tools, including standardized pair-counting
methods, to measure the correlations between the network communities and a set
of self-identified user characteristics (residence, class year, major, and high
school). We review the basic properties and statistics of the pair-counting
indices employed and recall, in simplified notation, a useful analytical
formula for the z-score of the Rand coefficient. Our study illustrates how to
examine different instances of social networks constructed in similar
environments, emphasizes the array of social forces that combine to form
"communities," and leads to comparative observations about online social lives
that can be used to infer comparisons about offline social structures. In our
illustration of this methodology, we calculate the relative contributions of
different characteristics to the community structure of individual universities
and subsequently compare these relative contributions at different
universities, measuring for example the importance of common high school
affiliation to large state universities and the varying degrees of influence
common major can have on the social structure at different universities. The
heterogeneity of communities that we observe indicates that these networks
typically have multiple organizing factors rather than a single dominant one.Comment: Version 3 (17 pages, 5 multi-part figures), accepted in SIAM Revie
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