48,715 research outputs found
BLADYG: A Graph Processing Framework for Large Dynamic Graphs
International audienceRecently, distributed processing of large dynamic graphs has become very popular , especially in certain domains such as social network analysis, Web graph analysis and spatial network analysis. In this context, many distributed/parallel graph processing systems have been proposed, such as Pregel, PowerGraph, GraphLab, and Trinity. However, these systems deal only with static graphs and do not consider the issue of processing evolving and dynamic graphs. In this paper, we are considering the issues of scale and dynamism in the case of graph processing systems. We present BLADYG, a graph processing framework that addresses the issue of dynamism in large-scale graphs. We present an implementation of BLADYG on top of akka framework. We experimentally evaluate the performance of the proposed framework by applying it to problems such as distributed k-core decomposition and partitioning of large dynamic graphs. The experimental results show that the performance and scalability of BLADYG are satisfying for large-scale dynamic graphs
A distributed, compact routing protocol for the Internet
The Internet has grown in size at rapid rates since BGP records began, and continues to do so. This has raised concerns about the scalability of the current BGP routing system, as the routing state at each router in a shortest-path routing protocol will grow at a supra-linearly rate as the network grows. The concerns are that the memory capacity of routers will not be able to keep up with demands, and that the growth of the Internet will become ever more cramped as more and more of the world seeks the benefits of being connected.
Compact routing schemes, where the routing state grows only sub-linearly relative to the growth of the network, could solve this problem and ensure that router memory would not be a bottleneck to Internet growth. These schemes trade away shortest-path routing for scalable memory state, by allowing some paths to have a certain amount of bounded “stretch”.
The most promising such scheme is Cowen Routing, which can provide scalable, compact routing state for Internet routing, while still providing shortest-path routing to nearly all other nodes, with only slightly stretched paths to a very small subset of the network. Currently, there is no fully distributed form of Cowen Routing that would be practical for the Internet.
This dissertation describes a fully distributed and compact protocol for Cowen routing, using the k-core graph decomposition.
Previous compact routing work showed the k-core graph decomposition is useful for Cowen Routing on the Internet, but no distributed form existed. This dissertation gives a distributed k-core algorithm optimised to be efficient on dynamic graphs, along with with proofs of its correctness. The performance and efficiency of this distributed k-core algorithm is evaluated on large, Internet AS graphs, with excellent results.
This dissertation then goes on to describe a fully distributed and compact Cowen Routing protocol. This protocol being comprised of a landmark selection process for Cowen Routing using the k-core algorithm, with mechanisms to ensure compact state at all times, including at bootstrap; a local cluster routing process, with mechanisms for policy application and control of cluster sizes, ensuring again that state can remain compact at all times; and a landmark routing process is described with a prioritisation mechanism for announcements that ensures compact state at all times
Fully Dynamic Algorithm for Top- Densest Subgraphs
Given a large graph, the densest-subgraph problem asks to find a subgraph
with maximum average degree. When considering the top- version of this
problem, a na\"ive solution is to iteratively find the densest subgraph and
remove it in each iteration. However, such a solution is impractical due to
high processing cost. The problem is further complicated when dealing with
dynamic graphs, since adding or removing an edge requires re-running the
algorithm. In this paper, we study the top- densest-subgraph problem in the
sliding-window model and propose an efficient fully-dynamic algorithm. The
input of our algorithm consists of an edge stream, and the goal is to find the
node-disjoint subgraphs that maximize the sum of their densities. In contrast
to existing state-of-the-art solutions that require iterating over the entire
graph upon any update, our algorithm profits from the observation that updates
only affect a limited region of the graph. Therefore, the top- densest
subgraphs are maintained by only applying local updates. We provide a
theoretical analysis of the proposed algorithm and show empirically that the
algorithm often generates denser subgraphs than state-of-the-art competitors.
Experiments show an improvement in efficiency of up to five orders of magnitude
compared to state-of-the-art solutions.Comment: 10 pages, 8 figures, accepted at CIKM 201
Shared-memory Graph Truss Decomposition
We present PKT, a new shared-memory parallel algorithm and OpenMP
implementation for the truss decomposition of large sparse graphs. A k-truss is
a dense subgraph definition that can be considered a relaxation of a clique.
Truss decomposition refers to a partitioning of all the edges in the graph
based on their k-truss membership. The truss decomposition of a graph has many
applications. We show that our new approach PKT consistently outperforms other
truss decomposition approaches for a collection of large sparse graphs and on a
24-core shared-memory server. PKT is based on a recently proposed algorithm for
k-core decomposition.Comment: 10 pages, conference submissio
Locally Estimating Core Numbers
Graphs are a powerful way to model interactions and relationships in data
from a wide variety of application domains. In this setting, entities
represented by vertices at the "center" of the graph are often more important
than those associated with vertices on the "fringes". For example, central
nodes tend to be more critical in the spread of information or disease and play
an important role in clustering/community formation. Identifying such "core"
vertices has recently received additional attention in the context of {\em
network experiments}, which analyze the response when a random subset of
vertices are exposed to a treatment (e.g. inoculation, free product samples,
etc). Specifically, the likelihood of having many central vertices in any
exposure subset can have a significant impact on the experiment.
We focus on using -cores and core numbers to measure the extent to which a
vertex is central in a graph. Existing algorithms for computing the core number
of a vertex require the entire graph as input, an unrealistic scenario in many
real world applications. Moreover, in the context of network experiments, the
subgraph induced by the treated vertices is only known in a probabilistic
sense. We introduce a new method for estimating the core number based only on
the properties of the graph within a region of radius around the
vertex, and prove an asymptotic error bound of our estimator on random graphs.
Further, we empirically validate the accuracy of our estimator for small values
of on a representative corpus of real data sets. Finally, we evaluate
the impact of improved local estimation on an open problem in network
experimentation posed by Ugander et al.Comment: Main paper body is identical to previous version (ICDM version).
Appendix with additional data sets and enlarged figures has been added to the
en
A Fast Order-Based Approach for Core Maintenance
Graphs have been widely used in many applications such as social networks,
collaboration networks, and biological networks. One important graph analytics
is to explore cohesive subgraphs in a large graph. Among several cohesive
subgraphs studied, k-core is one that can be computed in linear time for a
static graph. Since graphs are evolving in real applications, in this paper, we
study core maintenance which is to reduce the computational cost to compute
k-cores for a graph when graphs are updated from time to time dynamically. We
identify drawbacks of the existing efficient algorithm, which needs a large
search space to find the vertices that need to be updated, and has high
overhead to maintain the index built, when a graph is updated. We propose a new
order-based approach to maintain an order, called k-order, among vertices,
while a graph is updated. Our new algorithm can significantly outperform the
state-of-the-art algorithm up to 3 orders of magnitude for the 11 large real
graphs tested. We report our findings in this paper
Core Decomposition in Multilayer Networks: Theory, Algorithms, and Applications
Multilayer networks are a powerful paradigm to model complex systems, where
multiple relations occur between the same entities. Despite the keen interest
in a variety of tasks, algorithms, and analyses in this type of network, the
problem of extracting dense subgraphs has remained largely unexplored so far.
In this work we study the problem of core decomposition of a multilayer
network. The multilayer context is much challenging as no total order exists
among multilayer cores; rather, they form a lattice whose size is exponential
in the number of layers. In this setting we devise three algorithms which
differ in the way they visit the core lattice and in their pruning techniques.
We then move a step forward and study the problem of extracting the
inner-most (also known as maximal) cores, i.e., the cores that are not
dominated by any other core in terms of their core index in all the layers.
Inner-most cores are typically orders of magnitude less than all the cores.
Motivated by this, we devise an algorithm that effectively exploits the
maximality property and extracts inner-most cores directly, without first
computing a complete decomposition.
Finally, we showcase the multilayer core-decomposition tool in a variety of
scenarios and problems. We start by considering the problem of densest-subgraph
extraction in multilayer networks. We introduce a definition of multilayer
densest subgraph that trades-off between high density and number of layers in
which the high density holds, and exploit multilayer core decomposition to
approximate this problem with quality guarantees. As further applications, we
show how to utilize multilayer core decomposition to speed-up the extraction of
frequent cross-graph quasi-cliques and to generalize the community-search
problem to the multilayer setting
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