17,963 research outputs found
Network Sampling: From Static to Streaming Graphs
Network sampling is integral to the analysis of social, information, and
biological networks. Since many real-world networks are massive in size,
continuously evolving, and/or distributed in nature, the network structure is
often sampled in order to facilitate study. For these reasons, a more thorough
and complete understanding of network sampling is critical to support the field
of network science. In this paper, we outline a framework for the general
problem of network sampling, by highlighting the different objectives,
population and units of interest, and classes of network sampling methods. In
addition, we propose a spectrum of computational models for network sampling
methods, ranging from the traditionally studied model based on the assumption
of a static domain to a more challenging model that is appropriate for
streaming domains. We design a family of sampling methods based on the concept
of graph induction that generalize across the full spectrum of computational
models (from static to streaming) while efficiently preserving many of the
topological properties of the input graphs. Furthermore, we demonstrate how
traditional static sampling algorithms can be modified for graph streams for
each of the three main classes of sampling methods: node, edge, and
topology-based sampling. Our experimental results indicate that our proposed
family of sampling methods more accurately preserves the underlying properties
of the graph for both static and streaming graphs. Finally, we study the impact
of network sampling algorithms on the parameter estimation and performance
evaluation of relational classification algorithms
Framework for Clique-based Fusion of Graph Streams in Multi-function System Testing
The paper describes a framework for multi-function system testing.
Multi-function system testing is considered as fusion (or revelation) of
clique-like structures. The following sets are considered: (i) subsystems
(system parts or units / components / modules), (ii) system functions and a
subset of system components for each system function, and (iii) function
clusters (some groups of system functions which are used jointly). Test
procedures (as units testing) are used for each subsystem. The procedures lead
to an ordinal result (states, colors) for each component, e.g., [1,2,3,4]
(where 1 corresponds to 'out of service', 2 corresponds to 'major faults', 3
corresponds to 'minor faults', 4 corresponds to 'trouble free service'). Thus,
for each system function a graph over corresponding system components is
examined while taking into account ordinal estimates/colors of the components.
Further, an integrated graph (i.e., colored graph) for each function cluster is
considered (this graph integrates the graphs for corresponding system
functions). For the integrated graph (for each function cluster) structure
revelation problems are under examination (revelation of some subgraphs which
can lead to system faults): (1) revelation of clique and quasi-clique (by
vertices at level 1, 2, etc.; by edges/interconnection existence) and (2)
dynamical problems (when vertex colors are functions of time) are studied as
well: existence of a time interval when clique or quasi-clique can exist.
Numerical examples illustrate the approach and problems.Comment: 6 pages, 13 figure
Finding and tracking multi-density clusters in an online dynamic data stream
The file attached to this record is the author's final peer reviewed version.Change is one of the biggest challenges in dynamic stream mining. From a data-mining perspective, adapting and tracking change is desirable in order to understand how and why change has occurred. Clustering, a form of unsupervised learning, can be used to identify the underlying patterns in a stream. Density-based clustering identifies clusters as areas of high density separated by areas of low density. This paper proposes a Multi-Density Stream Clustering (MDSC) algorithm to address these two problems; the multi-density problem and the problem of discovering and tracking changes in a dynamic stream. MDSC consists of two on-line components; discovered, labelled clusters and an outlier buffer. Incoming points are assigned to a live cluster or passed to the outlier buffer. New clusters are discovered in the buffer using an ant-inspired swarm intelligence approach. The newly discovered cluster is uniquely labelled and added to the set of live clusters. Processed data is subject to an ageing function and will disappear when it is no longer relevant. MDSC is shown to perform favourably to state-of-the-art peer stream-clustering algorithms on a range of real and synthetic data-streams. Experimental results suggest that MDSC can discover qualitatively useful patterns while being scalable and robust to noise
Scalable Online Betweenness Centrality in Evolving Graphs
Betweenness centrality is a classic measure that quantifies the importance of
a graph element (vertex or edge) according to the fraction of shortest paths
passing through it. This measure is notoriously expensive to compute, and the
best known algorithm runs in O(nm) time. The problems of efficiency and
scalability are exacerbated in a dynamic setting, where the input is an
evolving graph seen edge by edge, and the goal is to keep the betweenness
centrality up to date. In this paper we propose the first truly scalable
algorithm for online computation of betweenness centrality of both vertices and
edges in an evolving graph where new edges are added and existing edges are
removed. Our algorithm is carefully engineered with out-of-core techniques and
tailored for modern parallel stream processing engines that run on clusters of
shared-nothing commodity hardware. Hence, it is amenable to real-world
deployment. We experiment on graphs that are two orders of magnitude larger
than previous studies. Our method is able to keep the betweenness centrality
measures up to date online, i.e., the time to update the measures is smaller
than the inter-arrival time between two consecutive updates.Comment: 15 pages, 9 Figures, accepted for publication in IEEE Transactions on
Knowledge and Data Engineerin
Initial Access in 5G mm-Wave Cellular Networks
The massive amounts of bandwidth available at millimeter-wave frequencies
(roughly above 10 GHz) have the potential to greatly increase the capacity of
fifth generation cellular wireless systems. However, to overcome the high
isotropic pathloss experienced at these frequencies, high directionality will
be required at both the base station and the mobile user equipment to establish
sufficient link budget in wide area networks. This reliance on directionality
has important implications for control layer procedures. Initial access in
particular can be significantly delayed due to the need for the base station
and the user to find the proper alignment for directional transmission and
reception. This paper provides a survey of several recently proposed techniques
for this purpose. A coverage and delay analysis is performed to compare various
techniques including exhaustive and iterative search, and Context Information
based algorithms. We show that the best strategy depends on the target SNR
regime, and provide guidelines to characterize the optimal choice as a function
of the system parameters.Comment: 6 pages, 3 figures, 3 tables, 15 references, submitted to IEEE COMMAG
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