80 research outputs found
Finding Community Structure with Performance Guarantees in Complex Networks
Many networks including social networks, computer networks, and biological
networks are found to divide naturally into communities of densely connected
individuals. Finding community structure is one of fundamental problems in
network science. Since Newman's suggestion of using \emph{modularity} as a
measure to qualify the goodness of community structures, many efficient methods
to maximize modularity have been proposed but without a guarantee of
optimality. In this paper, we propose two polynomial-time algorithms to the
modularity maximization problem with theoretical performance guarantees. The
first algorithm comes with a \emph{priori guarantee} that the modularity of
found community structure is within a constant factor of the optimal modularity
when the network has the power-law degree distribution. Despite being mainly of
theoretical interest, to our best knowledge, this is the first approximation
algorithm for finding community structure in networks. In our second algorithm,
we propose a \emph{sparse metric}, a substantially faster linear programming
method for maximizing modularity and apply a rounding technique based on this
sparse metric with a \emph{posteriori approximation guarantee}. Our experiments
show that the rounding algorithm returns the optimal solutions in most cases
and are very scalable, that is, it can run on a network of a few thousand nodes
whereas the LP solution in the literature only ran on a network of at most 235
nodes
Outward Influence and Cascade Size Estimation in Billion-scale Networks
Estimating cascade size and nodes' influence is a fundamental task in social,
technological, and biological networks. Yet this task is extremely challenging
due to the sheer size and the structural heterogeneity of networks. We
investigate a new influence measure, termed outward influence (OI), defined as
the (expected) number of nodes that a subset of nodes will activate,
excluding the nodes in S. Thus, OI equals, the de facto standard measure,
influence spread of S minus |S|. OI is not only more informative for nodes with
small influence, but also, critical in designing new effective sampling and
statistical estimation methods.
Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence
spread/outward influence at scale and with rigorous theoretical guarantees. The
proposed methods are built on two novel components 1) IICP an important
sampling method for outward influence, and 2) RSA, a robust mean estimation
method that minimize the number of samples through analyzing variance and range
of random variables. Compared to the state-of-the art for influence estimation,
SIEA is times faster in theory and up to several orders of
magnitude faster in practice. For the first time, influence of nodes in the
networks of billions of edges can be estimated with high accuracy within a few
minutes. Our comprehensive experiments on real-world networks also give
evidence against the popular practice of using a fixed number, e.g. 10K or 20K,
of samples to compute the "ground truth" for influence spread.Comment: 16 pages, SIGMETRICS 201
Importance Sketching of Influence Dynamics in Billion-scale Networks
The blooming availability of traces for social, biological, and communication
networks opens up unprecedented opportunities in analyzing diffusion processes
in networks. However, the sheer sizes of the nowadays networks raise serious
challenges in computational efficiency and scalability.
In this paper, we propose a new hyper-graph sketching framework for inflence
dynamics in networks. The central of our sketching framework, called SKIS, is
an efficient importance sampling algorithm that returns only non-singular
reverse cascades in the network. Comparing to previously developed sketches
like RIS and SKIM, our sketch significantly enhances estimation quality while
substantially reducing processing time and memory-footprint. Further, we
present general strategies of using SKIS to enhance existing algorithms for
influence estimation and influence maximization which are motivated by
practical applications like viral marketing. Using SKIS, we design high-quality
influence oracle for seed sets with average estimation error up to 10x times
smaller than those using RIS and 6x times smaller than SKIM. In addition, our
influence maximization using SKIS substantially improves the quality of
solutions for greedy algorithms. It achieves up to 10x times speed-up and 4x
memory reduction for the fastest RIS-based DSSA algorithm, while maintaining
the same theoretical guarantees.Comment: 12 pages, to appear in ICDM 2017 as a regular pape
Predicting Tensile Strength for Prestressed Reinforced Concrete-Driven Piles
Reinforced concrete piles installed by impact hammers have been used as a common solution for deep foundations because they are cost effective and require less time for construction. Driven piles are often used in large volumes for infrastructure and industrial projects in rural areas. Unlike other installation methods, installing piles using impact hammers can generate tensile stress during construction, which can result in pile failures. Induced tensile stress occurs when piles are being driven through a hard soil layer to a softer soil layer, and transverse cracks happen when induced tensile stress exceeds the pile tensile strength. This issue is not explicitly stated in most standards; the rare code that mentions this issue is AASHTO 2014. AASHTO 2014 uses correlations between the concrete tensile and compressive strengths to obtain the pile tensile strength. However, data collected from more than 1300 tests on the correlations between the concrete tensile and compressive strengths show that the concrete pile tensile strengths obtained using AASHTO 2014 are significantly conservative. This paper provides an adjustment in the correlation for the tensile strength based on previous data, and it proposes an approach to estimate the tensile strength for concrete-driven piles. A case study of the effects of pile failures on the tensile strength is also presented to verify the approach. The obtained tensile strength from the proposed approach agrees well with the measured field data. For the case study, the pile tensile strength obtained using the proposed approach is 38% and 59% higher than the tensile strength obtained using AASHTO 2014. These quantities are significant but may vary, depending on the compression strength of the concrete used and the pile configurations. The proposed approach better predicts the tensile strength of concrete piles and can lead to cost savings. View Full-Tex
Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance
We investigate the performance of multi-user multiple-antenna downlink
systems in which a BS serves multiple users via a shared wireless medium. In
order to fully exploit the spatial diversity while minimizing the passive
energy consumed by radio frequency (RF) components, the BS is equipped with M
RF chains and N antennas, where M < N. Upon receiving pilot sequences to obtain
the channel state information, the BS determines the best subset of M antennas
for serving the users. We propose a joint antenna selection and precoding
design (JASPD) algorithm to maximize the system sum rate subject to a transmit
power constraint and QoS requirements. The JASPD overcomes the non-convexity of
the formulated problem via a doubly iterative algorithm, in which an inner loop
successively optimizes the precoding vectors, followed by an outer loop that
tries all valid antenna subsets. Although approaching the (near) global
optimality, the JASPD suffers from a combinatorial complexity, which may limit
its application in real-time network operations. To overcome this limitation,
we propose a learning-based antenna selection and precoding design algorithm
(L-ASPA), which employs a DNN to establish underlaying relations between the
key system parameters and the selected antennas. The proposed L-ASPD is robust
against the number of users and their locations, BS's transmit power, as well
as the small-scale channel fading. With a well-trained learning model, it is
shown that the L-ASPD significantly outperforms baseline schemes based on the
block diagonalization and a learning-assisted solution for broadcasting systems
and achieves higher effective sum rate than that of the JASPA under limited
processing time. In addition, we observed that the proposed L-ASPD can reduce
the computation complexity by 95% while retaining more than 95% of the optimal
performance.Comment: accepted to the IEEE Transactions on Wireless Communication
Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework
To enable an intelligent, programmable and multi-vendor radio access network
(RAN) for 6G networks, considerable efforts have been made in standardization
and development of open RAN (O-RAN). So far, however, the applicability of
O-RAN in controlling and optimizing RAN functions has not been widely
investigated. In this paper, we jointly optimize the flow-split distribution,
congestion control and scheduling (JFCS) to enable an intelligent traffic
steering application in O-RAN. Combining tools from network utility
maximization and stochastic optimization, we introduce a multi-layer
optimization framework that provides fast convergence, long-term
utility-optimality and significant delay reduction compared to the
state-of-the-art and baseline RAN approaches. Our main contributions are
three-fold: i) we propose the novel JFCS framework to efficiently and
adaptively direct traffic to appropriate radio units; ii) we develop
low-complexity algorithms based on the reinforcement learning, inner
approximation and bisection search methods to effectively solve the JFCS
problem in different time scales; and iii) the rigorous theoretical performance
results are analyzed to show that there exists a scaling factor to improve the
tradeoff between delay and utility-optimization. Collectively, the insights in
this work will open the door towards fully automated networks with enhanced
control and flexibility. Numerical results are provided to demonstrate the
effectiveness of the proposed algorithms in terms of the convergence rate,
long-term utility-optimality and delay reduction.Comment: 15 pages, 10 figures. A short version will be submitted to IEEE
GLOBECOM 202
Federated Learning Meets Contract Theory: Energy-Efficient Framework for Electric Vehicle Networks
In this paper, we propose a novel energy-efficient framework for an electric
vehicle (EV) network using a contract theoretic-based economic model to
maximize the profits of charging stations (CSs) and improve the social welfare
of the network. Specifically, we first introduce CS-based and CS
clustering-based decentralized federated energy learning (DFEL) approaches
which enable the CSs to train their own energy transactions locally to predict
energy demands. In this way, each CS can exchange its learned model with other
CSs to improve prediction accuracy without revealing actual datasets and reduce
communication overhead among the CSs. Based on the energy demand prediction, we
then design a multi-principal one-agent (MPOA) contract-based method. In
particular, we formulate the CSs' utility maximization as a non-collaborative
energy contract problem in which each CS maximizes its utility under common
constraints from the smart grid provider (SGP) and other CSs' contracts. Then,
we prove the existence of an equilibrium contract solution for all the CSs and
develop an iterative algorithm at the SGP to find the equilibrium. Through
simulation results using the dataset of CSs' transactions in Dundee city, the
United Kingdom between 2017 and 2018, we demonstrate that our proposed method
can achieve the energy demand prediction accuracy improvement up to 24.63% and
lessen communication overhead by 96.3% compared with other machine learning
algorithms. Furthermore, our proposed method can outperform non-contract-based
economic models by 35% and 36% in terms of the CSs' utilities and social
welfare of the network, respectively.Comment: 16 pages, submitted to TM
Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework
peer reviewedTo enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN). So far, however, the applicability of O-RAN in controlling and optimizing RAN functions has not been widely investigated. In this paper, we jointly optimize the flow-split distribution, congestion control and scheduling (JFCS) to enable an intelligent traffic steering application in O-RAN. Combining tools from network utility maximization and stochastic optimization, we introduce a multi-layer optimization framework that provides fast convergence, long-term utility-optimality and significant delay reduction compared to the state-of-the-art and baseline RAN approaches. Our main contributions are three-fold: i ) we propose the novel JFCS framework to efficiently and adaptively direct traffic to appropriate radio units; ii ) we develop low-complexity algorithms based on the reinforcement learning, inner approximation and bisection search methods to effectively solve the JFCS problem in different time scales; and iii ) the rigorous theoretical performance results are analyzed to show that there exists a scaling factor to improve the tradeoff between delay and utility-optimization. Collectively, the insights in this work will open the door towards fully automated networks with enhanced control and flexibility. Numerical results are provided to demonstrate the effectiveness of the proposed algorithms in terms of the convergence rate, long-term utility-optimality and delay reduction
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