3,868 research outputs found
Correlations in Bipartite Collaboration Networks
Collaboration networks are studied as an example of growing bipartite
networks. These have been previously observed to have structure such as
positive correlations between nearest-neighbour degrees. However, a detailed
understanding of the origin of this phenomenon and the growth dynamics is
lacking. Both of these are analyzed empirically and simulated using various
models. A new one is presented, incorporating empirically necessary ingredients
such as bipartiteness and sublinear preferential attachment. This, and a
recently proposed model of team assembly both agree roughly with some empirical
observations and fail in several others.Comment: 13 pages, 17 figures, 2 table, submitted to JSTAT; manuscript
reorganized, figures and a table adde
Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users
In this paper, the problem of proactive caching is studied for cloud radio
access networks (CRANs). In the studied model, the baseband units (BBUs) can
predict the content request distribution and mobility pattern of each user,
determine which content to cache at remote radio heads and BBUs. This problem
is formulated as an optimization problem which jointly incorporates backhaul
and fronthaul loads and content caching. To solve this problem, an algorithm
that combines the machine learning framework of echo state networks with
sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs
can predict each user's content request distribution and mobility pattern while
having only limited information on the network's and user's state. In order to
predict each user's periodic mobility pattern with minimal complexity, the
memory capacity of the corresponding ESN is derived for a periodic input. This
memory capacity is shown to be able to record the maximum amount of user
information for the proposed ESN model. Then, a sublinear algorithm is proposed
to determine which content to cache while using limited content request
distribution samples. Simulation results using real data from Youku and the
Beijing University of Posts and Telecommunications show that the proposed
approach yields significant gains, in terms of sum effective capacity, that
reach up to 27.8% and 30.7%, respectively, compared to random caching with
clustering and random caching without clustering algorithm.Comment: Accepted in the IEEE Transactions on Wireless Communication
SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search
The -Nearest Neighbor Search (-NNS) is the backbone of several
cloud-based services such as recommender systems, face recognition, and
database search on text and images. In these services, the client sends the
query to the cloud server and receives the response in which case the query and
response are revealed to the service provider. Such data disclosures are
unacceptable in several scenarios due to the sensitivity of data and/or privacy
laws.
In this paper, we introduce SANNS, a system for secure -NNS that keeps
client's query and the search result confidential. SANNS comprises two
protocols: an optimized linear scan and a protocol based on a novel sublinear
time clustering-based algorithm. We prove the security of both protocols in the
standard semi-honest model. The protocols are built upon several
state-of-the-art cryptographic primitives such as lattice-based additively
homomorphic encryption, distributed oblivious RAM, and garbled circuits. We
provide several contributions to each of these primitives which are applicable
to other secure computation tasks. Both of our protocols rely on a new circuit
for the approximate top- selection from numbers that is built from comparators.
We have implemented our proposed system and performed extensive experimental
results on four datasets in two different computation environments,
demonstrating more than faster response time compared to
optimally implemented protocols from the prior work. Moreover, SANNS is the
first work that scales to the database of 10 million entries, pushing the limit
by more than two orders of magnitude.Comment: 18 pages, to appear at USENIX Security Symposium 202
Massively Parallel Algorithms for Distance Approximation and Spanners
Over the past decade, there has been increasing interest in
distributed/parallel algorithms for processing large-scale graphs. By now, we
have quite fast algorithms -- usually sublogarithmic-time and often
-time, or even faster -- for a number of fundamental graph
problems in the massively parallel computation (MPC) model. This model is a
widely-adopted theoretical abstraction of MapReduce style settings, where a
number of machines communicate in an all-to-all manner to process large-scale
data. Contributing to this line of work on MPC graph algorithms, we present
round MPC algorithms for computing
-spanners in the strongly sublinear regime of local memory. To
the best of our knowledge, these are the first sublogarithmic-time MPC
algorithms for spanner construction. As primary applications of our spanners,
we get two important implications, as follows:
-For the MPC setting, we get an -round algorithm for
approximation of all pairs shortest paths (APSP) in the
near-linear regime of local memory. To the best of our knowledge, this is the
first sublogarithmic-time MPC algorithm for distance approximations.
-Our result above also extends to the Congested Clique model of distributed
computing, with the same round complexity and approximation guarantee. This
gives the first sub-logarithmic algorithm for approximating APSP in weighted
graphs in the Congested Clique model
Estimating the weight of metric minimum spanning trees in sublinear time
In this paper we present a sublinear-time -approximation randomized algorithm to estimate the weight of the minimum spanning tree of an -point metric space. The running time of the algorithm is . Since the full description of an -point metric space is of size , the complexity of our algorithm is sublinear with respect to the input size. Our algorithm is almost optimal as it is not possible to approximate in time the weight of the minimum spanning tree to within any factor. We also show that no deterministic algorithm can achieve a -approximation in time. Furthermore, it has been previously shown that no algorithm exists that returns a spanning tree whose weight is within a constant times the optimum
Faster Separators for Shallow Minor-Free Graphs via Dynamic Approximate Distance Oracles
Plotkin, Rao, and Smith (SODA'97) showed that any graph with edges and
vertices that excludes as a depth -minor has a
separator of size and that such a separator can be
found in time. A time bound of for
any constant was later given (W., FOCS'11) which is an
improvement for non-sparse graphs. We give three new algorithms. The first has
the same separator size and running time O(\mbox{poly}(h)\ell
m^{1+\epsilon}). This is a significant improvement for small and .
If for an arbitrarily small chosen constant
, we get a time bound of O(\mbox{poly}(h)\ell n^{1+\epsilon}).
The second algorithm achieves the same separator size (with a slightly larger
polynomial dependency on ) and running time O(\mbox{poly}(h)(\sqrt\ell
n^{1+\epsilon} + n^{2+\epsilon}/\ell^{3/2})) when . Our third algorithm has running time
O(\mbox{poly}(h)\sqrt\ell n^{1+\epsilon}) when . It finds a separator of size O(n/\ell) + \tilde
O(\mbox{poly}(h)\ell\sqrt n) which is no worse than previous bounds when
is fixed and . A main tool in obtaining our results
is a novel application of a decremental approximate distance oracle of Roditty
and Zwick.Comment: 16 pages. Full version of the paper that appeared at ICALP'14. Minor
fixes regarding the time bounds such that these bounds hold also for
non-sparse graph
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