305,521 research outputs found
A Multi-signal Variant for the GPU-based Parallelization of Growing Self-Organizing Networks
Among the many possible approaches for the parallelization of self-organizing
networks, and in particular of growing self-organizing networks, perhaps the
most common one is producing an optimized, parallel implementation of the
standard sequential algorithms reported in the literature. In this paper we
explore an alternative approach, based on a new algorithm variant specifically
designed to match the features of the large-scale, fine-grained parallelism of
GPUs, in which multiple input signals are processed at once. Comparative tests
have been performed, using both parallel and sequential implementations of the
new algorithm variant, in particular for a growing self-organizing network that
reconstructs surfaces from point clouds. The experimental results show that
this approach allows harnessing in a more effective way the intrinsic
parallelism that the self-organizing networks algorithms seem intuitively to
suggest, obtaining better performances even with networks of smaller size.Comment: 17 page
Self-Organizing Flows in Social Networks
Social networks offer users new means of accessing information, essentially
relying on "social filtering", i.e. propagation and filtering of information by
social contacts. The sheer amount of data flowing in these networks, combined
with the limited budget of attention of each user, makes it difficult to ensure
that social filtering brings relevant content to the interested users. Our
motivation in this paper is to measure to what extent self-organization of the
social network results in efficient social filtering. To this end we introduce
flow games, a simple abstraction that models network formation under selfish
user dynamics, featuring user-specific interests and budget of attention. In
the context of homogeneous user interests, we show that selfish dynamics
converge to a stable network structure (namely a pure Nash equilibrium) with
close-to-optimal information dissemination. We show in contrast, for the more
realistic case of heterogeneous interests, that convergence, if it occurs, may
lead to information dissemination that can be arbitrarily inefficient, as
captured by an unbounded "price of anarchy". Nevertheless the situation differs
when users' interests exhibit a particular structure, captured by a metric
space with low doubling dimension. In that case, natural autonomous dynamics
converge to a stable configuration. Moreover, users obtain all the information
of interest to them in the corresponding dissemination, provided their budget
of attention is logarithmic in the size of their interest set
Recommended from our members
Self-organizing peer-to-peer social networks
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 The Authors.Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.National Natural Science of Foundation of Chin
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Self-organizing social hierarchies on scale-free networks
In this work we extend the model of Bonabeau et al. in the case of scale-free
networks. A sharp transition is observed from an egalitarian to an hierarchical
society, with a very low population density threshold. The exact threshold
value also depends on the network size. We find that in an hierarchical society
the number of individuals with strong winning attitude is much lower than the
number of the community members that have a low winning probability
Triadic motifs and dyadic self-organization in the World Trade Network
In self-organizing networks, topology and dynamics coevolve in a continuous
feedback, without exogenous driving. The World Trade Network (WTN) is one of
the few empirically well documented examples of self-organizing networks: its
topology strongly depends on the GDP of world countries, which in turn depends
on the structure of trade. Therefore, understanding which are the key
topological properties of the WTN that deviate from randomness provides direct
empirical information about the structural effects of self-organization. Here,
using an analytical pattern-detection method that we have recently proposed, we
study the occurrence of triadic "motifs" (subgraphs of three vertices) in the
WTN between 1950 and 2000. We find that, unlike other properties, motifs are
not explained by only the in- and out-degree sequences. By contrast, they are
completely explained if also the numbers of reciprocal edges are taken into
account. This implies that the self-organization process underlying the
evolution of the WTN is almost completely encoded into the dyadic structure,
which strongly depends on reciprocity.Comment: 12 pages, 3 figures; Best Paper Award at the 6th International
Conference on Self-Organizing Systems, Delft, The Netherlands, 15-16/03/201
- …