6,317 research outputs found
Game Theoretic Formation of a Centrality Based Network
We model the formation of networks as a game where players aspire to maximize
their own centrality by increasing the number of other players to which they
are path-wise connected, while simultaneously incurring a cost for each added
adjacent edge. We simulate the interactions between players using an algorithm
that factors in rational strategic behavior based on a common objective
function. The resulting networks exhibit pairwise stability, from which we
derive necessary stable conditions for specific graph topologies. We then
expand the model to simulate non-trivial games with large numbers of players.
We show that using conditions necessary for the stability of star topologies we
can induce the formation of hub players that positively impact the total
welfare of the network.Comment: Submitted to 2012 ASE Social Informatics Conferenc
Efficient computation of the Shapley value for game-theoretic network centrality
The Shapley value—probably the most important normative payoff division scheme in coalitional games—has recently been advocated as a useful measure of centrality in networks. However, although this approach has a variety of real-world applications (including social and organisational networks, biological networks and communication networks), its computational properties have not been widely studied. To date, the only practicable approach to compute Shapley value-based centrality has been via Monte Carlo simulations which are computationally expensive and not guaranteed to give an exact answer. Against this background, this paper presents the first study of the computational aspects of the Shapley value for network centralities. Specifically, we develop exact analytical formulae for Shapley value-based centrality in both weighted and unweighted networks and develop efficient (polynomial time) and exact algorithms based on them. We empirically evaluate these algorithms on two real-life examples (an infrastructure network representing the topology of the Western States Power Grid and a collaboration network from the field of astrophysics) and demonstrate that they deliver significant speedups over the Monte Carlo approach. Fo
Offloading Content with Self-organizing Mobile Fogs
Mobile users in an urban environment access content on the internet from
different locations. It is challenging for the current service providers to
cope with the increasing content demand from a large number of collocated
mobile users. In-network caching to offload content at nodes closer to users
alleviate the issue, though efficient cache management is required to find out
who should cache what, when and where in an urban environment, given nodes
limited computing, communication and caching resources. To address this, we
first define a novel relation between content popularity and availability in
the network and investigate a node's eligibility to cache content based on its
urban reachability. We then allow nodes to self-organize into mobile fogs to
increase the distributed cache and maximize content availability in a
cost-effective manner. However, to cater rational nodes, we propose a coalition
game for the nodes to offer a maximum "virtual cache" assuming a monetary
reward is paid to them by the service/content provider. Nodes are allowed to
merge into different spatio-temporal coalitions in order to increase the
distributed cache size at the network edge. Results obtained through
simulations using realistic urban mobility trace validate the performance of
our caching system showing a ratio of 60-85% of cache hits compared to the
30-40% obtained by the existing schemes and 10% in case of no coalition
Social networks: Prestige, centrality, and influence (Invited paper)
We deliver a short overview of di erent centrality measures and influence concepts in social networks, and present the relation-algebraic approach to the concepts of power and influence. First, we briefly discuss four kinds of measures of centrality: the ones based on degree, closeness, betweenness, and the eigenvector-related measures. We consider centrality of a node and of a network. Moreover, we give a classi cation of the centrality measures based on a topology of network flows. Furthermore, we present a certain model of influence in a social network and discuss some applications of relation algebra and RelView to this model.social network ; centrality ; prestige ; influence ; relation algebra ; RelView
A Content-based Centrality Metric for Collaborative Caching in Information-Centric Fogs
Information-Centric Fog Computing enables a multitude of nodes near the
end-users to provide storage, communication, and computing, rather than in the
cloud. In a fog network, nodes connect with each other directly to get content
locally whenever possible. As the topology of the network directly influences
the nodes' connectivity, there has been some work to compute the graph
centrality of each node within that network topology. The centrality is then
used to distinguish nodes in the fog network, or to prioritize some nodes over
others to participate in the caching fog. We argue that, for an
Information-Centric Fog Computing approach, graph centrality is not an
appropriate metric. Indeed, a node with low connectivity that caches a lot of
content may provide a very valuable role in the network.
To capture this, we introduce acontent-based centrality (CBC) metric which
takes into account how well a node is connected to the content the network is
delivering, rather than to the other nodes in the network. To illustrate the
validity of considering content-based centrality, we use this new metric for a
collaborative caching algorithm. We compare the performance of the proposed
collaborative caching with typical centrality based, non-centrality based, and
non-collaborative caching mechanisms. Our simulation implements CBC on three
instances of large scale realistic network topology comprising 2,896 nodes with
three content replication levels. Results shows that CBC outperforms benchmark
caching schemes and yields a roughly 3x improvement for the average cache hit
rate
Assortative Mixing Equilibria in Social Network Games
It is known that individuals in social networks tend to exhibit homophily
(a.k.a. assortative mixing) in their social ties, which implies that they
prefer bonding with others of their own kind. But what are the reasons for this
phenomenon? Is it that such relations are more convenient and easier to
maintain? Or are there also some more tangible benefits to be gained from this
collective behaviour?
The current work takes a game-theoretic perspective on this phenomenon, and
studies the conditions under which different assortative mixing strategies lead
to equilibrium in an evolving social network. We focus on a biased preferential
attachment model where the strategy of each group (e.g., political or social
minority) determines the level of bias of its members toward other group
members and non-members. Our first result is that if the utility function that
the group attempts to maximize is the degree centrality of the group,
interpreted as the sum of degrees of the group members in the network, then the
only strategy achieving Nash equilibrium is a perfect homophily, which implies
that cooperation with other groups is harmful to this utility function. A
second, and perhaps more surprising, result is that if a reward for inter-group
cooperation is added to the utility function (e.g., externally enforced by an
authority as a regulation), then there are only two possible equilibria,
namely, perfect homophily or perfect heterophily, and it is possible to
characterize their feasibility spaces. Interestingly, these results hold
regardless of the minority-majority ratio in the population.
We believe that these results, as well as the game-theoretic perspective
presented herein, may contribute to a better understanding of the forces that
shape the groups and communities of our society
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