165 research outputs found
GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra
We propose GraphMineSuite (GMS): the first benchmarking suite for graph
mining that facilitates evaluating and constructing high-performance graph
mining algorithms. First, GMS comes with a benchmark specification based on
extensive literature review, prescribing representative problems, algorithms,
and datasets. Second, GMS offers a carefully designed software platform for
seamless testing of different fine-grained elements of graph mining algorithms,
such as graph representations or algorithm subroutines. The platform includes
parallel implementations of more than 40 considered baselines, and it
facilitates developing complex and fast mining algorithms. High modularity is
possible by harnessing set algebra operations such as set intersection and
difference, which enables breaking complex graph mining algorithms into simple
building blocks that can be separately experimented with. GMS is supported with
a broad concurrency analysis for portability in performance insights, and a
novel performance metric to assess the throughput of graph mining algorithms,
enabling more insightful evaluation. As use cases, we harness GMS to rapidly
redesign and accelerate state-of-the-art baselines of core graph mining
problems: degeneracy reordering (by up to >2x), maximal clique listing (by up
to >9x), k-clique listing (by 1.1x), and subgraph isomorphism (by up to 2.5x),
also obtaining better theoretical performance bounds
Network Survivability Analysis: Coarse-Graining And Graph-Theoretic Strategies
In this dissertation, the interplay between geographic information about the network and the principal properties and structure of the underlying graph are used to quantify the structural and functional survivability of the network. This work focuses on the local aspect of survivability by studying the propagation of loss in the network as a function of the distance of the fault from a given origin-destination node pair
Online Social Networks: Measurements, Analysis and Solutions for Mining Challenges
In the last decade, online social networks showed enormous growth. With the rise
of these networks and the consequent availability of wealth social network data, Social
Network Analysis (SNA) led researchers to get the opportunity to access, analyse and
mine the social behaviour of millions of people, explore the way they communicate and
exchange information.
Despite the growing interest in analysing social networks, there are some challenges
and implications accompanying the analysis and mining of these networks. For example,
dealing with large-scale and evolving networks is not yet an easy task and still requires
a new mining solution. In addition, finding communities within these networks is a
challenging task and could open opportunities to see how people behave in groups on a
large scale. Also, the challenge of validating and optimizing communities without knowing
in advance the structure of the network due to the lack of ground truth is yet another
challenging barrier for validating the meaningfulness of the resulting communities.
In this thesis, we started by providing an overview of the necessary background and key
concepts required in the area of social networks analysis. Our main focus is to provide
solutions to tackle the key challenges in this area. For doing so, first, we introduce a predictive
technique to help in the prediction of the execution time of the analysis tasks for
evolving networks through employing predictive modeling techniques to the problem of
evolving and large-scale networks. Second, we study the performance of existing community
detection approaches to derive high quality community structure using a real email
network through analysing the exchange of emails and exploring community dynamics.
The aim is to study the community behavioral patterns and evaluate their quality within
an actual network. Finally, we propose an ensemble technique for deriving communities
using a rich internal enterprise real network in IBM that reflects real collaborations
and communications between employees. The technique aims to improve the community
detection process through the fusion of different algorithms
The Swiss Board Directors Network in 2009
We study the networks formed by the directors of the most important Swiss
boards and the boards themselves for the year 2009. The networks are obtained
by projection from the original bipartite graph. We highlight a number of
important statistical features of those networks such as degree distribution,
weight distribution, and several centrality measures as well as their
interrelationships. While similar statistics were already known for other board
systems, and are comparable here, we have extended the study with a careful
investigation of director and board centrality, a k-core analysis, and a
simulation of the speed of information propagation and its relationships with
the topological aspects of the network such as clustering and link weight and
betweenness. The overall picture that emerges is one in which the topological
structure of the Swiss board and director networks has evolved in such a way
that special actors and links between actors play a fundamental role in the
flow of information among distant parts of the network. This is shown in
particular by the centrality measures and by the simulation of a simple
epidemic process on the directors network.Comment: Submitted to The European Physical Journal
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