77 research outputs found
Randomized Algorithms for Tracking Distributed Count, Frequencies, and Ranks
We show that randomization can lead to significant improvements for a few
fundamental problems in distributed tracking. Our basis is the {\em
count-tracking} problem, where there are players, each holding a counter
that gets incremented over time, and the goal is to track an
\eps-approximation of their sum continuously at all times,
using minimum communication. While the deterministic communication complexity
of the problem is \Theta(k/\eps \cdot \log N), where is the final value
of when the tracking finishes, we show that with randomization, the
communication cost can be reduced to \Theta(\sqrt{k}/\eps \cdot \log N). Our
algorithm is simple and uses only O(1) space at each player, while the lower
bound holds even assuming each player has infinite computing power. Then, we
extend our techniques to two related distributed tracking problems: {\em
frequency-tracking} and {\em rank-tracking}, and obtain similar improvements
over previous deterministic algorithms. Both problems are of central importance
in large data monitoring and analysis, and have been extensively studied in the
literature.Comment: 19 pages, 1 figur
Coalitional Games with Overlapping Coalitions for Interference Management in Small Cell Networks
In this paper, we study the problem of cooperative interference management in
an OFDMA two-tier small cell network. In particular, we propose a novel
approach for allowing the small cells to cooperate, so as to optimize their
sum-rate, while cooperatively satisfying their maximum transmit power
constraints. Unlike existing work which assumes that only disjoint groups of
cooperative small cells can emerge, we formulate the small cells' cooperation
problem as a coalition formation game with overlapping coalitions. In this
game, each small cell base station can choose to participate in one or more
cooperative groups (or coalitions) simultaneously, so as to optimize the
tradeoff between the benefits and costs associated with cooperation. We study
the properties of the proposed overlapping coalition formation game and we show
that it exhibits negative externalities due to interference. Then, we propose a
novel decentralized algorithm that allows the small cell base stations to
interact and self-organize into a stable overlapping coalitional structure.
Simulation results show that the proposed algorithm results in a notable
performance advantage in terms of the total system sum-rate, relative to the
noncooperative case and the classical algorithms for coalitional games with
non-overlapping coalitions
Dynamic Self-training Framework for Graph Convolutional Networks
Graph neural networks (GNN) such as GCN, GAT, MoNet have achieved
state-of-the-art results on semi-supervised learning on graphs. However, when
the number of labeled nodes is very small, the performances of GNNs downgrade
dramatically. Self-training has proved to be effective for resolving this
issue, however, the performance of self-trained GCN is still inferior to that
of G2G and DGI for many settings. Moreover, additional model complexity make it
more difficult to tune the hyper-parameters and do model selection. We argue
that the power of self-training is still not fully explored for the node
classification task. In this paper, we propose a unified end-to-end
self-training framework called \emph{Dynamic Self-traning}, which generalizes
and simplifies prior work. A simple instantiation of the framework based on GCN
is provided and empirical results show that our framework outperforms all
previous methods including GNNs, embedding based method and self-trained GCNs
by a noticeable margin. Moreover, compared with standard self-training,
hyper-parameter tuning for our framework is easier.Comment: 11page
GB-KMV: An Augmented KMV Sketch for Approximate Containment Similarity Search
In this paper, we study the problem of approximate containment similarity
search. Given two records Q and X, the containment similarity between Q and X
with respect to Q is |Q intersect X|/ |Q|. Given a query record Q and a set of
records S, the containment similarity search finds a set of records from S
whose containment similarity regarding Q are not less than the given threshold.
This problem has many important applications in commercial and scientific
fields such as record matching and domain search. Existing solution relies on
the asymmetric LSH method by transforming the containment similarity to
well-studied Jaccard similarity. In this paper, we use a different framework by
transforming the containment similarity to set intersection. We propose a novel
augmented KMV sketch technique, namely GB-KMV, which is data-dependent and can
achieve a good trade-off between the sketch size and the accuracy. We provide a
set of theoretical analysis to underpin the proposed augmented KMV sketch
technique, and show that it outperforms the state-of-the-art technique LSH-E in
terms of estimation accuracy under practical assumption. Our comprehensive
experiments on real-life datasets verify that GB-KMV is superior to LSH-E in
terms of the space-accuracy trade-off, time-accuracy trade-off, and the sketch
construction time. For instance, with similar estimation accuracy (F-1 score),
GB-KMV is over 100 times faster than LSH-E on some real-life dataset
Communication complexity of approximate maximum matching in the message-passing model
We consider the communication complexity of finding an approximate maximum matching in a graph in a multi-party message-passing communication model. The maximum matching problem is one of the most fundamental graph combinatorial problems, with a variety of applications. The input to the problem is a graph G that has n vertices and the set of edges partitioned over k sites, and an approximation ratio parameter α. The output is required to be a matching in G that has to be reported by one of the sites, whose size is at least factor α of the size of a maximum matching in G. We show that the communication complexity of this problem is Ω(α2kn)information bits. This bound is shown to be tight up to a log n factor, by constructing an algorithm, establishing its correctness, and an upper bound on the communication cost. The lower bound also applies to other graph combinatorial problems in the message-passing communication model, including max-flow and graph sparsification
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