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of
12
Network Structure and Travel Time Perception
, 2011
"... Research on travel behavior has traditionally focused on ways that infrastructure investments, namely, the urban form and built environment, can be used to influence travel. Proponents argue that overall travel can be reduced by bringing the trip origins and destination closer. Horning et al. (2008) ..."
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Cited by 6 (4 self)
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Research on travel behavior has traditionally focused on ways that infrastructure investments, namely, the urban form and built environment, can be used to influence travel. Proponents argue that overall travel can be reduced by bringing the trip origins and destination closer. Horning et al. (2008) point out that the inherent assumption underlying this argument is the
STREAMER: a distributed framework for incremental closeness centrality computation
 In Proc. of IEEE Cluster
, 2013
"... Abstract—Networks are commonly used to model the traffic patterns, social interactions, or web pages. The nodes in a network do not possess the same characteristics: some nodes are naturally more connected and some nodes can be more important. Closeness centrality (CC) is a global metric that quanti ..."
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Cited by 4 (2 self)
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Abstract—Networks are commonly used to model the traffic patterns, social interactions, or web pages. The nodes in a network do not possess the same characteristics: some nodes are naturally more connected and some nodes can be more important. Closeness centrality (CC) is a global metric that quantifies how important is a given node in the network. When the network is dynamic and keeps changing, the relative importance of the nodes also changes. The best known algorithm to compute the CC scores makes it impractical to recompute them from scratch after each modification. In this paper, we propose STREAMER, a distributed memory framework for incrementally maintaining the closeness centrality scores of a network upon changes. It leverages pipelined and replicated parallelism and takes NUMA effects into account. It speeds up the maintenance of the CC of a real graph with 916K vertices and 4.3M edges by a factor of 497 using a 64 nodes cluster. I.
Incremental Algorithms for Closeness Centrality 2 IEEE BigData’13
"... citation graphs • Facebook has a billion users and a trillion connections • Twitter has more than 200 million users • Who
is
more
important
in a network?
Who
controls the flow
between
nodes? • Centrality
metrics
answer these quesAons • Closeness
Centrality
( ..."
Abstract

Cited by 1 (0 self)
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citation graphs • Facebook has a billion users and a trillion connections • Twitter has more than 200 million users • Who
is
more
important
in a network?
Who
controls the flow
between
nodes? • Centrality
metrics
answer these quesAons • Closeness
Centrality
(CC)
is an intriguing
metric • How
to
handle
changes? • Incremental
algorithms
are essenAal Incremental Algorithms
for
Closeness
Centrality
3 IEEE BigData’13
Revisiting Edge and Node Parallelism for Dynamic
"... Abstract—Betweenness Centrality is a widely used graph analytic that has applications such as finding influential people in social networks, analyzing power grids, and studying protein interactions. However, its complexity makes its exact computation infeasible for large graphs of interest. Furtherm ..."
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Abstract—Betweenness Centrality is a widely used graph analytic that has applications such as finding influential people in social networks, analyzing power grids, and studying protein interactions. However, its complexity makes its exact computation infeasible for large graphs of interest. Furthermore, networks tend to change over time, invalidating previously calculated results and encouraging new analyses regarding how centrality metrics vary with time. While GPUs have dominated regular, structured application domains, their high memory throughput and massive parallelism has made them a suitable target architecture for irregular, unstructured applications as well. In this paper we compare and contrast two GPU implementations of an algorithm for dynamic betweenness centrality. We show that typical network updates affect the centrality scores of a surprisingly small subset of the total number of vertices in the graph. By efficiently mapping threads to units of work we achieve up to a 110x speedup over a CPU implementation of the algorithm and can update the analytic 45x faster on average than a static recomputation on the GPU. I.
9 THE FORM OF GENTRIFICATION Common morphological patterns in five gentrified areas of London, UK.
"... Many socioeconomic studies have been carried out to explain the phenomenon of gentrification. Although results of these works shed light on the process around this phenomenon, a perspective which focuses on the relationship between city form and gentrification is still missing. With this paper we tr ..."
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Many socioeconomic studies have been carried out to explain the phenomenon of gentrification. Although results of these works shed light on the process around this phenomenon, a perspective which focuses on the relationship between city form and gentrification is still missing. With this paper we try to address this gap by studying and comparing, through classic methods of mathematical statistics, morphological features of five London gentrified neighbourhoods. Outcomes confirm that areas which have undergone gentrification display similar and recognizable morphological patterns in terms of urban type and geographical location of main and local roads as well as businesses. These initial results confirm findings from previous research in urban sociology, and highlight the role of urban form in contributing to shape dynamics of nonspatial nature in cities.
Large(r) Networks and Centrality
"... citation graphs • Facebook has a billion users and a trillion connections • Twitter has more than 200 million users ..."
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citation graphs • Facebook has a billion users and a trillion connections • Twitter has more than 200 million users
Noname manuscript No. (will be inserted by the editor) Can a Black Hole Collapse to a Spacetime Singularity?
, 704
"... Abstract A critique of the singularity theorems of Penrose, Hawking, and Geroch is given. It is pointed out that a gravitationally collapsing black hole acts as an ultrahigh energy particle accelerator that can accelerate particles to energies inconceivable in any terrestrial particle accelerator, a ..."
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Abstract A critique of the singularity theorems of Penrose, Hawking, and Geroch is given. It is pointed out that a gravitationally collapsing black hole acts as an ultrahigh energy particle accelerator that can accelerate particles to energies inconceivable in any terrestrial particle accelerator, and that when the energy E of the particles comprising matter in a black hole is ∼ 10 2 GeV or more, or equivalently, the temperature T is ∼ 10 15 K or more, the entire matter in the black hole is converted into quarkgluon plasma permeated by leptons. As quarks and leptons are fermions, it is emphasized that the collapse of a blackhole to a spacetime singularity is inhibited by Pauli’s exclusion principle. It is also suggested that ultimately a black hole may end up either as a stable quark star, or as a pulsating quark star which may be a source of gravitational radiation, or it may simply explode with a mini bang of a sort. Keywords black hole · gravitational collapse · spacetime singularity · quark star 1
Incremental Closeness Centrality in Distributed Memory
, 2015
"... Networks are commonly used to model traffic patterns, social interactions, or web pages. The vertices in a network do not possess the same characteristics: some vertices are naturally more connected and some vertices can be more important. Closeness centrality (CC) is a global metric that quantifies ..."
Abstract
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Networks are commonly used to model traffic patterns, social interactions, or web pages. The vertices in a network do not possess the same characteristics: some vertices are naturally more connected and some vertices can be more important. Closeness centrality (CC) is a global metric that quantifies how important is a given vertex in the network. When the network is dynamic and keeps changing, the relative importance of the vertices also changes. The best known algorithm to compute the CC scores makes it impractical to recompute them from scratch after each modification. In this paper, we propose Streamer, a distributed memory framework for incrementally maintaining the closeness centrality scores of a network upon changes. It leverages pipelined, replicated parallelism, and SpMMbased BFSs, and it takes NUMA effects into account. It makes maintaining the Closeness Centrality values of reallife networks with millions of interactions significantly faster and obtains almost linear speedups on a 64 nodes 8 threads/node cluster.
Several multiplexes in the same city: The role of socioeconomic differences
"... in urban mobility ..."
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