499,078 research outputs found
Topic and Role Discovery in Social Networks
Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the language content or topics on those links. We present the Author- Recipient-Topic (ART) model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) and the Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly on both the sender and recipient—steering the discovery of topics according to the relationships between people. We give results on both the Enron email corpus and a researcher’s email archive, providing evidence not only that clearly relevant topics are discovered, but that the ART model better predicts people’s roles
On Heterogeneous Neighbor Discovery in Wireless Sensor Networks
Neighbor discovery plays a crucial role in the formation of wireless sensor
networks and mobile networks where the power of sensors (or mobile devices) is
constrained. Due to the difficulty of clock synchronization, many asynchronous
protocols based on wake-up scheduling have been developed over the years in
order to enable timely neighbor discovery between neighboring sensors while
saving energy. However, existing protocols are not fine-grained enough to
support all heterogeneous battery duty cycles, which can lead to a more rapid
deterioration of long-term battery health for those without support. Existing
research can be broadly divided into two categories according to their
neighbor-discovery techniques---the quorum based protocols and the co-primality
based protocols.In this paper, we propose two neighbor discovery protocols,
called Hedis and Todis, that optimize the duty cycle granularity of quorum and
co-primality based protocols respectively, by enabling the finest-grained
control of heterogeneous duty cycles. We compare the two optimal protocols via
analytical and simulation results, which show that although the optimal
co-primality based protocol (Todis) is simpler in its design, the optimal
quorum based protocol (Hedis) has a better performance since it has a lower
relative error rate and smaller discovery delay, while still allowing the
sensor nodes to wake up at a more infrequent rate.Comment: Accepted by IEEE INFOCOM 201
Nucleotide second messengers in bacterial decision making
Since the initial discovery of bacterial nucleotide second messengers (NSMs), we have made huge progress towards understanding these complex signalling networks. Many NSM networks contain dozens of metabolic enzymes and binding targets, whose activity is tightly controlled at every regulatory level. They function as global regulators and in specific signalling circuits, controlling multiple aspects of bacterial behaviour and development. Despite these advances there is much still to discover, with current research focussing on the molecular mechanisms of signalling circuits, the role of the environment in controlling NSM pathways and attempts to understand signalling at the whole cell/community level. Here we examine recent developments in the NSM signalling field and discuss their implications for understanding this important driver of microbial behaviour
Visual Analysis of Complex Networks for Business Intelligence with Gephi
International audiencePlatforms which combine data mining algorithms and interactive visualizations play a key role in the discovery process from complex networks data, e.g. Web and Online Social Networks data. Here we illustrate the use of Gephi, an open source software for networks visual exploration, for the visual analysis of Business Intelligence data modeled as complex networks
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