1,767 research outputs found
Community Detection and Growth Potential Prediction from Patent Citation Networks
The scoring of patents is useful for technology management analysis.
Therefore, a necessity of developing citation network clustering and prediction
of future citations for practical patent scoring arises. In this paper, we
propose a community detection method using the Node2vec. And in order to
analyze growth potential we compare three ''time series analysis methods'', the
Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of
our experiments, we could find common technical points from those clusters by
Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model
was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author
Shortest Path Computation with No Information Leakage
Shortest path computation is one of the most common queries in location-based
services (LBSs). Although particularly useful, such queries raise serious
privacy concerns. Exposing to a (potentially untrusted) LBS the client's
position and her destination may reveal personal information, such as social
habits, health condition, shopping preferences, lifestyle choices, etc. The
only existing method for privacy-preserving shortest path computation follows
the obfuscation paradigm; it prevents the LBS from inferring the source and
destination of the query with a probability higher than a threshold. This
implies, however, that the LBS still deduces some information (albeit not
exact) about the client's location and her destination. In this paper we aim at
strong privacy, where the adversary learns nothing about the shortest path
query. We achieve this via established private information retrieval
techniques, which we treat as black-box building blocks. Experiments on real,
large-scale road networks assess the practicality of our schemes.Comment: VLDB201
Stability of shortest paths in complex networks with random edge weights
We study shortest paths and spanning trees of complex networks with random
edge weights. Edges which do not belong to the spanning tree are inactive in a
transport process within the network. The introduction of quenched disorder
modifies the spanning tree such that some edges are activated and the network
diameter is increased. With analytic random-walk mappings and numerical
analysis, we find that the spanning tree is unstable to the introduction of
disorder and displays a phase-transition-like behavior at zero disorder
strength . In the infinite network-size limit (), we
obtain a continuous transition with the density of activated edges
growing like and with the diameter-expansion coefficient
growing like in the regular network, and
first-order transitions with discontinuous jumps in and at
for the small-world (SW) network and the Barab\'asi-Albert
scale-free (SF) network. The asymptotic scaling behavior sets in when , where the crossover size scales as for the
regular network, for the SW network, and
for the SF network. In a
transient regime with , there is an infinite-order transition with
for the SW network
and for the SF network. It
shows that the transport pattern is practically most stable in the SF network.Comment: 9 pages, 7 figur
Distributed Approximation Algorithms for Weighted Shortest Paths
A distributed network is modeled by a graph having nodes (processors) and
diameter . We study the time complexity of approximating {\em weighted}
(undirected) shortest paths on distributed networks with a {\em
bandwidth restriction} on edges (the standard synchronous \congest model). The
question whether approximation algorithms help speed up the shortest paths
(more precisely distance computation) was raised since at least 2004 by Elkin
(SIGACT News 2004). The unweighted case of this problem is well-understood
while its weighted counterpart is fundamental problem in the area of
distributed approximation algorithms and remains widely open. We present new
algorithms for computing both single-source shortest paths (\sssp) and
all-pairs shortest paths (\apsp) in the weighted case.
Our main result is an algorithm for \sssp. Previous results are the classic
-time Bellman-Ford algorithm and an -time
-approximation algorithm, for any integer
, which follows from the result of Lenzen and Patt-Shamir (STOC 2013).
(Note that Lenzen and Patt-Shamir in fact solve a harder problem, and we use
to hide the O(\poly\log n) term.) We present an -time -approximation algorithm for \sssp. This
algorithm is {\em sublinear-time} as long as is sublinear, thus yielding a
sublinear-time algorithm with almost optimal solution. When is small, our
running time matches the lower bound of by Das Sarma
et al. (SICOMP 2012), which holds even when , up to a
\poly\log n factor.Comment: Full version of STOC 201
Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure
Large-scale transitions in societies are associated with both individual
behavioural change and restructuring of the social network. These two factors
have often been considered independently, yet recent advances in social network
research challenge this view. Here we show that common features of societal
marginalization and clustering emerge naturally during transitions in a
co-evolutionary adaptive network model. This is achieved by explicitly
considering the interplay between individual interaction and a dynamic network
structure in behavioural selection. We exemplify this mechanism by simulating
how smoking behaviour and the network structure get reconfigured by changing
social norms. Our results are consistent with empirical findings: The
prevalence of smoking was reduced, remaining smokers were preferentially
connected among each other and formed increasingly marginalised clusters. We
propose that self-amplifying feedbacks between individual behaviour and dynamic
restructuring of the network are main drivers of the transition. This
generative mechanism for co-evolution of individual behaviour and social
network structure may apply to a wide range of examples beyond smoking.Comment: 16 pages, 5 figure
Griffiths phases in infinite-dimensional, non-hierarchical modular networks
Griffiths phases (GPs), generated by the heterogeneities on modular networks,
have recently been suggested to provide a mechanism, rid of fine parameter
tuning, to explain the critical behavior of complex systems. One conjectured
requirement for systems with modular structures was that the network of modules
must be hierarchically organized and possess finite dimension. We investigate
the dynamical behavior of an activity spreading model, evolving on
heterogeneous random networks with highly modular structure and organized
non-hierarchically. We observe that loosely coupled modules act as effective
rare-regions, slowing down the extinction of activation. As a consequence, we
find extended control parameter regions with continuously changing dynamical
exponents for single network realizations, preserved after finite size
analyses, as in a real GP. The avalanche size distributions of spreading events
exhibit robust power-law tails. Our findings relax the requirement of
hierarchical organization of the modular structure, which can help to
rationalize the criticality of modular systems in the framework of GPs.Comment: 14 pages, 8 figure
Module hierarchy and centralisation in the anatomy and dynamics of human cortex
Systems neuroscience has recently unveiled numerous fundamental features of the macroscopic architecture of the human brain, the connectome, and we are beginning to understand how characteristics of brain dynamics emerge from the underlying anatomical connectivity. The current work utilises complex network analysis on a high-resolution structural connectivity of the human cortex to identify generic organisation principles, such as centralised, modular and hierarchical properties, as well as specific areas that are pivotal in shaping cortical dynamics and function.
After confirming its small-world and modular architecture, we characterise the cortex’ multilevel modular hierarchy, which appears to be reasonably centralised towards the brain’s strong global structural core. The potential functional importance of the core and hub regions is assessed by various complex network metrics, such as integration measures, network vulnerability and motif spectrum analysis.
Dynamics facilitated by the large-scale cortical topology is explored by simulating coupled oscillators on the anatomical connectivity. The results indicate that cortical connectivity appears to favour high dynamical complexity over high synchronizability. Taking the ability to entrain other brain regions as a proxy for the threat posed by a potential epileptic focus in a given region, we also show that epileptic foci in topologically more central areas should pose a higher epileptic threat than foci in more peripheral areas.
To assess the influence of macroscopic brain anatomy in shaping global resting state dynamics on slower time scales, we compare empirically obtained functional connectivity data with data from simulating dynamics on the structural connectivity. Despite considerable micro-scale variability between the two functional connectivities, our simulations are able to approximate the profile of the empirical functional connectivity.
Our results outline the combined characteristics a hierarchically modular and reasonably centralised macroscopic architecture of the human cerebral cortex, which, through these topological attributes, appears to facilitate highly complex dynamics and fundamentally shape brain function
Efficient successor retrieval operations for aggregate query processing on clustered road networks
Cataloged from PDF version of article.Get-Successors (GS) which retrieves all successors of a junction is a kernel operation used to facilitate aggregate computations in road network queries. Efficient implementation of the GS operation is crucial since the disk access cost of this operation constitutes a considerable portion of the total query processing cost. Firstly, we propose a new successor retrieval operation Get-Unevaluated-Successors (GUS), which retrieves only the unevaluated successors of a given junction. The GUS operation is an efficient implementation of the GS operation, where the candidate successors to be retrieved are pruned according to the properties and state of the algorithm. Secondly, we propose a hypergraph-based model for clustering successively retrieved junctions by the GUS operations to the same pages. The proposed model utilizes query logs to correctly capture the disk access cost of GUS operations. The proposed GUS operation and associated clustering model are evaluated for two different instances of GUS operations which typically arise in Dijkstra's single source shortest path algorithm and incremental network expansion framework. Our simulation results show that the proposed successor retrieval operation together with the proposed clustering hypergraph model is quite effective in reducing the number of disk accesses in query processing. (C) 2010 Published by Elsevier Inc
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