Dagstuhl Seminar Proceedings, Volume 5361
Dagstuhl Seminar Proceedings
DagSemProc
https://www.dagstuhl.de/dagpub/1862-4405
https://dblp.org/db/series/dagstuhl
1862-4405
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
5361
2006
https://drops.dagstuhl.de/entities/volume/DagSemProc-volume-5361
05361 Abstracts Collection – Algorithmic Aspects of Large and Complex Networks
From 04.09.05 to 09.09.05, the Dagstuhl Seminar 05361 ``Algorithmic Aspects of Large and Complex Networks'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available.
Algorithms
Large and Complex Networks
1-19
Regular Paper
Stefano
Leonardi
Stefano Leonardi
Friedhelm
Meyer auf der Heide
Friedhelm Meyer auf der Heide
Dorothea
Wagner
Dorothea Wagner
10.4230/DagSemProc.05361.1
Creative Commons Attribution 4.0 International license
https://creativecommons.org/licenses/by/4.0/legalcode
A Cost Mechanism for Fair Pricing of Resource Usage
We propose a simple and intuitive cost mechanism which assigns costs for the competitive usage of $m$ resources by $n$ selfish agents. Each agent has an individual demand; demands are drawn according to some probability distribution. The cost paid by an agent for a resource she chooses is the total demand put on the resource divided by the number of agents who chose that same resource. So, resources charge costs in an equitable, fair way, while each resource makes no profit out of the agents.
We call our model the Fair Pricing model. Its fair cost mechanism induces a non-cooperative game among the agents. To evaluate the Nash equilibria of this game, we introduce the Diffuse Price of Anarchy, as an extension of the Price of Anarchy that takes into account the probability distribution on the demands. We prove:
(1) Pure Nash equilibria may not exist, unless all chosen demands are identical; in contrast, a fully mixed Nash equilibrium exists for all possible choices of the demands. Further on, the fully mixed Nash equilibrium is the unique Nash equilibrium in case there are only two agents.
(2) In the worst-case choice of demands, the Price of Anarchy is $Theta (n)$;
for the special case of two agents, the Price of Anarchy is less than $2 - frac{1}{m}$.
(3) Assume now that demands are drawn from a bounded, independent probability distribution, where all demands are identically distributed and each is at most a (universal for the class) constant times its expectation. Then, the Diffuse Price of Anarchy is at most that same constant, which is just $2$ when each demand is distributed symmetrically around its expectation.
Cost Sharing
Diffuse Price of Anarchy
Fair Pricing
Resources
1-15
Regular Paper
Marios
Mavronicolas
Marios Mavronicolas
Panagiota
Panagopoulou
Panagiota Panagopoulou
Paul G.
Spirakis
Paul G. Spirakis
10.4230/DagSemProc.05361.2
Creative Commons Attribution 4.0 International license
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A Hybrid Model for Drawing Dynamic and Evolving Graphs
Dynamic processes frequently occur in many applications. Visualizations of dynamically evolving data, for example as part of the data analysis, are typically restricted to a cumulative static view or an animation/sequential view. Both methods have their benefits and are often complementary in their use. We present a hybrid model that combines the two techniques. This is accomplished by 2.5D drawings which are calculated in an incremental way. The method has been evaluated on collaboration networks.
Visualization dynamic/evolving graphs 2.5D
1-12
Regular Paper
Marco
Gaertler
Marco Gaertler
Dorothea
Wagner
Dorothea Wagner
10.4230/DagSemProc.05361.3
Creative Commons Attribution 4.0 International license
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Computing earliest arrival flows with multiple sources
Earliest arrival flows are motivated by applications related to
evacuation. Given a network with capacities and transit times on
the arcs, a subset of source nodes with supplies and a sink node,
the task is to send the given supplies from the sources to the sink
"as quickly as possible". The latter requirement is made more
precise by the earliest arrival property which requires that the
total amount of flow that has arrived at the sink is maximal for all
points in time simultaneously.
It is a classical result from the 1970s that, for the special case
of a single source node, earliest arrival flows do exist and can be
computed by essentially applying the Successive Shortest Path
Algorithm for min-cost flow computations. While it has previously
been observed that an earliest arrival flow still exists for
multiple sources, the problem of computing one efficiently has been
open. We present an exact algorithm for this problem whose running
time is strongly polynomial in the input plus output size of the
problem.
Networks
flows over time
dynamic flows
earliest arrival
evacuation
1-3
Regular Paper
Nadine
Baumann
Nadine Baumann
Martin
Skutella
Martin Skutella
10.4230/DagSemProc.05361.4
Creative Commons Attribution 4.0 International license
https://creativecommons.org/licenses/by/4.0/legalcode
Cost Sharing Mechanisms for Fair Pricing of Resources Usage
We propose a simple and intuitive cost mechanism which assigns costs for the competitive usage of $m$ resources by $n$ selfish agents. Each agent has an individual demand; demands are drawn according to some probability distribution. The cost paid by an agent for a resource she chooses is the total demand put on the resource divided by the number of agents who chose that same resource. So, resources charge costs in an equitable, fair way, while each resource makes no profit out of the agents.
We call our model the Fair Pricing model. Its fair cost mechanism induces a non-cooperative game among the agents. To evaluate the Nash equilibria of this game, we introduce the Diffuse Price of Anarchy, as an extension of the Price of Anarchy that takes into account the probability distribution on the demands. We prove:
(1) Pure Nash equilibria may not exist, unless all chosen demands are identical. In contrast, we have been able to prove that pure Nash equilibria do exist for two closely related cost sharing models, namely the Average Cost Pricing and the Serial Cost Sharing models.
(2) A fully mixed Nash equilibrium exists for all possible choices of the demands. Further on, the fully mixed Nash equilibrium is the unique Nash equilibrium in case there are only two agents.
(3) In the worst-case choice of demands, the Price of Anarchy is $Theta (n)$; for the special case of two agents, the Price of Anarchy is less than $2 - frac{1}{m}$.
(4) Assume now that demands are drawn from a bounded, independent probability distribution, where all demands are identically distributed and each is at most a (universal for the class) constant times its expectation. Then, the Diffuse Price of Anarchy is at most that same constant, which is just 2 when each demand is distributed symmetrically around its expectation.
Cost Sharing
Diffuse Price of Anarchy
Fair Pricing
Resources
1-18
Regular Paper
Marios
Mavronicolas
Marios Mavronicolas
Panagiota
Panagopoulou
Panagiota Panagopoulou
Paul G.
Spirakis
Paul G. Spirakis
10.4230/DagSemProc.05361.5
Creative Commons Attribution 4.0 International license
https://creativecommons.org/licenses/by/4.0/legalcode
Deterministic boundary recongnition and topology extraction for large sensor networks
We present a new framework for the crucial challenge of
self-organization of a large sensor network. The basic scenario can
be described as follows: Given a large swarm of immobile sensor
nodes that have been scattered in a polygonal region, such as a
street network. Nodes have no knowledge of size or shape of the
environment or the position of other nodes. Moreover, they have no
way of measuring coordinates, geometric distances to other nodes, or
their direction. Their only way of interacting with other nodes is
to send or to receive messages from any node that is within
communication range. The objective is to develop algorithms and
protocols that allow self-organization of the swarm into large-scale
structures that reflect the structure of the street network, setting
the stage for global routing, tracking and guiding algorithms.
Our algorithms work in two stages: boundary recognition and topology
extraction. All steps are strictly deterministic, yield fast
distributed algorithms, and make no assumption on the distribution
of nodes in the environment, other than sufficient density.
Distributed algorithms
sensor networks
boundary recognition
topology extraction
1-0
Regular Paper
Sándor
Fekete
Sándor Fekete
Alexander
Kröller
Alexander Kröller
Dennis
Pfisterer
Dennis Pfisterer
Stefan
Fischer
Stefan Fischer
10.4230/DagSemProc.05361.6
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Force-Directed Approaches to Sensor Network Localization
In many sensor network applications it is necessary to compute low-error localization of the sensor nodes. Although embedding a GPS unit on each node would solve the problem for many outdoor applications, the cost of this solution for large networks is prohibitively high.
We consider static and mobile network localization approaches that make use of the local neighborhood information, in the form of relative distances and angles to nearby nodes, gathered through simpler and less costly devices (RF, ultrasound based range sensors, or
antenna arrays). Our algorithms do not make any assumptions about the existence of anchor nodes capable of locating themselves, nor about the knowledge of an initial localization to start with. Instead, we rely on a multi-scale force-directed approach, utilizing range and angle data through dead reckoning. We show that our localization algorithms are robust and scale well with network size.
Sensor network localization
multi-scale force-directed approach
dead reckoning
1-11
Regular Paper
Stephen G.
Kobourov
Stephen G. Kobourov
Alon
Efrat
Alon Efrat
David
Forrester
David Forrester
Anand
Iyer
Anand Iyer
10.4230/DagSemProc.05361.7
Creative Commons Attribution 4.0 International license
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Friends for Free: Self-Organizing Artificial Social Networks for Trust and Cooperation
By harvesting friendship networks from e-mail contacts or instant message "buddy lists" Peer-to-Peer (P2P) applications can improve performance in low trust environments such as the Internet. However, natural social networks are not always suitable, reliable or available. We propose an algorithm (SLACER) that allows peer nodes to create and manage their own friendship networks. We evaluate performance using a canonical test application, requiring cooperation between peers for socially optimal outcomes. The Artificial Social Networks (ASN) produced are connected, cooperative and robust - possessing many of the disable properties of human friendship networks such as trust between friends (directly linked peers) and short paths linking everyone via a chain of friends. In addition to new application possibilities, SLACER could supply ASN to P2P applications that currently depend on human social networks thus transforming them into fully autonomous, self-managing systems.
Evolution of cooperation
Evolving Networks
P2P
Prisoner's Dilemma
Tags
1-20
Regular Paper
David
Hales
David Hales
Stefano
Arteconi
Stefano Arteconi
10.4230/DagSemProc.05361.8
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