134,406 research outputs found
Gossip at Work: Unsanctioned Evaluative Talk in Formal School Meetings
This article uses a form of linguistic ethnography to analyze videotaped recordings of gossip that took place during formal school meetings. By comparing this gossip data against existing models of gossip based on data collected in informal settings, we identify eleven new response classes, including four forms of indirectness that operate to cloak gossip under ambiguity, and seven forms of avoidance that change the trajectory of gossip. In doing so, this article makes three larger contributions. First, it opens a new front in research on organizational politics by providing an empirically grounded, conceptually rich vocabulary for analyzing gossip in formal contexts. Second, it contributes to knowledge about social interactions in organizations. By examining gossip talk embedded within a work context, this project highlights the nexus between structure, agency, and interaction. Third, it contributes to understandings of gossip in general. By examining gossip in a context previously unexamined, this project provides analytical leverage for theorizing conditions under which gossip is likely and when it will take various forms
Gossip on Weighted Networks
We investigate how suitable a weighted network is for gossip spreading. The
proposed model is based on the gossip spreading model introduced by Lind et.al.
on unweighted networks. Weight represents "friendship." Potential spreader
prefers not to spread if the victim of gossip is a "close friend". Gossip
spreading is related to the triangles and cascades of triangles. It gives more
insight about the structure of a network.
We analyze gossip spreading on real weighted networks of human interactions.
6 co-occurrence and 7 social pattern networks are investigated. Gossip
propagation is found to be a good parameter to distinguish co-occurrence and
social pattern networks. As a comparison some miscellaneous networks and
computer generated networks based on ER, BA, WS models are also investigated.
They are found to be quite different than the human interaction networks.Comment: 8 pages, 4 figures, 1 tabl
Gossip Codes for Fingerprinting: Construction, Erasure Analysis and Pirate Tracing
This work presents two new construction techniques for q-ary Gossip codes
from tdesigns and Traceability schemes. These Gossip codes achieve the shortest
code length specified in terms of code parameters and can withstand erasures in
digital fingerprinting applications. This work presents the construction of
embedded Gossip codes for extending an existing Gossip code into a bigger code.
It discusses the construction of concatenated codes and realisation of erasure
model through concatenated codes.Comment: 28 page
Geographic Gossip: Efficient Averaging for Sensor Networks
Gossip algorithms for distributed computation are attractive due to their
simplicity, distributed nature, and robustness in noisy and uncertain
environments. However, using standard gossip algorithms can lead to a
significant waste in energy by repeatedly recirculating redundant information.
For realistic sensor network model topologies like grids and random geometric
graphs, the inefficiency of gossip schemes is related to the slow mixing times
of random walks on the communication graph. We propose and analyze an
alternative gossiping scheme that exploits geographic information. By utilizing
geographic routing combined with a simple resampling method, we demonstrate
substantial gains over previously proposed gossip protocols. For regular graphs
such as the ring or grid, our algorithm improves standard gossip by factors of
and respectively. For the more challenging case of random
geometric graphs, our algorithm computes the true average to accuracy
using radio
transmissions, which yields a factor improvement over
standard gossip algorithms. We illustrate these theoretical results with
experimental comparisons between our algorithm and standard methods as applied
to various classes of random fields.Comment: To appear, IEEE Transactions on Signal Processin
Equational Reasonings in Wireless Network Gossip Protocols
Gossip protocols have been proposed as a robust and efficient method for
disseminating information throughout large-scale networks. In this paper, we
propose a compositional analysis technique to study formal probabilistic models
of gossip protocols expressed in a simple probabilistic timed process calculus
for wireless sensor networks. We equip the calculus with a simulation theory to
compare probabilistic protocols that have similar behaviour up to a certain
tolerance. The theory is used to prove a number of algebraic laws which
revealed to be very effective to estimate the performances of gossip networks,
with and without communication collisions, and randomised gossip networks. Our
simulation theory is an asymmetric variant of the weak bisimulation metric that
maintains most of the properties of the original definition. However, our
asymmetric version is particularly suitable to reason on protocols in which the
systems under consideration are not approximately equivalent, as in the case of
gossip protocols
Greedy Gossip with Eavesdropping
This paper presents greedy gossip with eavesdropping (GGE), a novel
randomized gossip algorithm for distributed computation of the average
consensus problem. In gossip algorithms, nodes in the network randomly
communicate with their neighbors and exchange information iteratively. The
algorithms are simple and decentralized, making them attractive for wireless
network applications. In general, gossip algorithms are robust to unreliable
wireless conditions and time varying network topologies. In this paper we
introduce GGE and demonstrate that greedy updates lead to rapid convergence. We
do not require nodes to have any location information. Instead, greedy updates
are made possible by exploiting the broadcast nature of wireless
communications. During the operation of GGE, when a node decides to gossip,
instead of choosing one of its neighbors at random, it makes a greedy
selection, choosing the node which has the value most different from its own.
In order to make this selection, nodes need to know their neighbors' values.
Therefore, we assume that all transmissions are wireless broadcasts and nodes
keep track of their neighbors' values by eavesdropping on their communications.
We show that the convergence of GGE is guaranteed for connected network
topologies. We also study the rates of convergence and illustrate, through
theoretical bounds and numerical simulations, that GGE consistently outperforms
randomized gossip and performs comparably to geographic gossip on
moderate-sized random geometric graph topologies.Comment: 25 pages, 7 figure
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