98,905 research outputs found
Stochastic Programming with Primal-Dual Dynamics: A Mean-Field Game Approach
This study addresses primal-dual dynamics for a stochastic programming
problem for capacity network design. It is proven that consensus can be
achieved on the \textit{here and now} variables which represent the capacity of
the network. The main contribution is a heuristic approach which involves the
formulation of the problem as a mean-field game. Every agent in the mean-field
game has control over its own primal-dual dynamics and seeks consensus with
neighboring agents according to a communication topology. We obtain theoretical
results concerning the existence of a mean-field equilibrium. Moreover, we
prove that the consensus dynamics converge such that the agents agree on the
capacity of their respective micro-networks. Lastly, we emphasize how penalties
on control and state influence the dynamics of agents in the mean-field game
Evolutionary Dynamics of Information Diffusion over Social Networks
Current social networks are of extremely large-scale generating tremendous
information flows at every moment. How information diffuse over social networks
has attracted much attention from both industry and academics. Most of the
existing works on information diffusion analysis are based on machine learning
methods focusing on social network structure analysis and empirical data
mining. However, the dynamics of information diffusion, which are heavily
influenced by network users' decisions, actions and their socio-economic
interactions, is generally ignored by most of existing works. In this paper, we
propose an evolutionary game theoretic framework to model the dynamic
information diffusion process in social networks. Specifically, we derive the
information diffusion dynamics in complete networks, uniform degree and
non-uniform degree networks, with the highlight of two special networks,
Erd\H{o}s-R\'enyi random network and the Barab\'asi-Albert scale-free network.
We find that the dynamics of information diffusion over these three kinds of
networks are scale-free and the same with each other when the network scale is
sufficiently large. To verify our theoretical analysis, we perform simulations
for the information diffusion over synthetic networks and real-world Facebook
networks. Moreover, we also conduct experiment on Twitter hashtags dataset,
which shows that the proposed game theoretic model can well fit and predict the
information diffusion over real social networks.Comment: arXiv admin note: substantial text overlap with arXiv:1309.292
Signed Network Formation Games and Clustering Balance
We propose a signed network formation game, in which pairs of individuals
strategically change the signs of the edges in a complete network. These
individuals are members of a social network who strategically reduce cognitive
dissonances by changing their interpersonal appraisals. We characterize the
best-response dynamics for this game and prove that its implementation \pc{can}
dynamically drive the network to a sociologically meaningful sign configuration
called clustering balance. In this configuration, agents in the social network
form one or more clusters that have positive relationships among their members
but negative relationships among members of other clusters. In the past,
various researchers in the fields of psycho-sociology, political science, and
physics have looked at models that explain the generation of up to two
clusters. Our work contributes to these fields by proposing a simple model that
generates a broader class of signed networks.Comment: 15 pages, 3 figure
Evolutionary Information Diffusion over Social Networks
Social networks have become ubiquitous in our daily life, as such it has
attracted great research interests recently. A key challenge is that it is of
extremely large-scale with tremendous information flow, creating the phenomenon
of "Big Data". Under such a circumstance, understanding information diffusion
over social networks has become an important research issue. Most of the
existing works on information diffusion analysis are based on either network
structure modeling or empirical approach with dataset mining. However, the
information diffusion is also heavily influenced by network users' decisions,
actions and their socio-economic connections, which is generally ignored in
existing works. In this paper, we propose an evolutionary game theoretic
framework to model the dynamic information diffusion process in social
networks. Specifically, we analyze the framework in uniform degree and
non-uniform degree networks and derive the closed-form expressions of the
evolutionary stable network states. Moreover, the information diffusion over
two special networks, Erd\H{o}s-R\'enyi random network and the
Barab\'asi-Albert scale-free network, are also highlighted. To verify our
theoretical analysis, we conduct experiments by using both synthetic networks
and real-world Facebook network, as well as real-world information spreading
dataset of Twitter and Memetracker. Experiments shows that the proposed game
theoretic framework is effective and practical in modeling the social network
users' information forwarding behaviors
A Systematic Framework and Characterization of Influence-Based Network Centrality
In this paper, we present a framework for studying the following fundamental
question in network analysis: How should one assess the centralities of nodes
in an information/influence propagation process over a social network?
Our framework systematically extends a family of classical graph-theoretical
centrality formulations, including degree centrality, harmonic centrality, and
their "sphere-of-influence" generalizations, to influence-based network
centralities. We further extend natural group centralities from graph models to
influence models, since group cooperation is essential in social influences.
This in turn enables us to assess individuals' centralities in group influence
settings by applying the concept of Shapley value from cooperative game theory.
Mathematically, using the property that these centrality formulations are
Bayesian, we prove the following characterization theorem: Every
influence-based centrality formulation in this family is the unique Bayesian
centrality that conforms with its corresponding graph-theoretical centrality
formulation. Moreover, the uniqueness is fully determined by the centrality
formulation on the class of layered graphs, which is derived from a beautiful
algebraic structure of influence instances modeled by cascading sequences. Our
main mathematical result that layered graphs in fact form a basis for the space
of influence-cascading-sequence profiles could also be useful in other studies
of network influences. We further provide an algorithmic framework for
efficient approximation of these influence-based centrality measures.
Our study provides a systematic road map for comparative analyses of
different influence-based centrality formulations, as well as for transferring
graph-theoretical concepts to influence models
A Complete framework for ambush avoidance in realistic environments
Operating vehicles in adversarial environments between a recurring
origin-destination pair requires new planning techniques. A two players
zero-sum game is introduced. The goal of the first player is to minimize the
expected casualties undergone by a convoy. The goal of the second player is to
maximize this damage. The outcome of the game is obtained via a linear program
that solves the corresponding minmax optimization problem over this outcome.
Different environment models are defined in order to compute routing strategies
over unstructured environments. To compare these methods for increasingly
accurate representations of the environment, a grid-based model is chosen to
represent the environment and the existence of a sufficient network size is
highlighted. A global framework for the generation of realistic routing
strategies between any two points is described. This framework requires a good
assessment of the potential casualties at any location, therefore the most
important parameters are identified. Finally the framework is tested on real
world environments
Cooperation Optimized Design for Information Dissemination in Vehicular Networks using Evolutionary Game Theory
We present an evolutionary game theoretic approach to study node cooperation
behavior in wireless ad hoc networks. Evolutionary game theory (EGT) has been
used to study the conditions governing the growth of cooperation behavior in
biological and social networks. We propose a model of node cooperation behavior
in dynamic wireless networks such as vehicular networks. Our work is motivated
by the fact that, similar to existing EGT studies, node behavior in dynamic
wireless networks is characterized by decision making that only depends on the
immediate neighborhood. We adapt our model to study cooperation behavior in the
context of information dissemination in wireless networks. We obtain conditions
that determine whether a network evolves to a state of complete cooperation
from all nodes. Finally, we use our model to study the evolution of cooperation
behavior and its impact on content downloading in vehicular networks, taking
into consideration realistic network conditions
Network Essence: PageRank Completion and Centrality-Conforming Markov Chains
Ji\v{r}\'i Matou\v{s}ek (1963-2015) had many breakthrough contributions in
mathematics and algorithm design. His milestone results are not only profound
but also elegant. By going beyond the original objects --- such as Euclidean
spaces or linear programs --- Jirka found the essence of the challenging
mathematical/algorithmic problems as well as beautiful solutions that were
natural to him, but were surprising discoveries to the field.
In this short exploration article, I will first share with readers my initial
encounter with Jirka and discuss one of his fundamental geometric results from
the early 1990s. In the age of social and information networks, I will then
turn the discussion from geometric structures to network structures, attempting
to take a humble step towards the holy grail of network science, that is to
understand the network essence that underlies the observed
sparse-and-multifaceted network data. I will discuss a simple result which
summarizes some basic algebraic properties of personalized PageRank matrices.
Unlike the traditional transitive closure of binary relations, the personalized
PageRank matrices take "accumulated Markovian closure" of network data. Some of
these algebraic properties are known in various contexts. But I hope featuring
them together in a broader context will help to illustrate the desirable
properties of this Markovian completion of networks, and motivate systematic
developments of a network theory for understanding vast and ubiquitous
multifaceted network data.Comment: In "A Journey Through Discrete Mathematics, A Tribute to Ji\v{r}\'i
Matou\v{s}ek", Editors Martin Loebl, Jaroslav Ne\v{s}et\v{r}il and Robin
Thomas, Springer International Publishing, 201
Deep Neural Networks for Optimal Team Composition
Cooperation is a fundamental social mechanism, whose effects on human
performance have been investigated in several environments. Online games are
modern-days natural settings in which cooperation strongly affects human
behavior. Every day, millions of players connect and play together in
team-based games: the patterns of cooperation can either foster or hinder
individual skill learning and performance. This work has three goals: (i)
identifying teammates' influence on players' performance in the short and long
term, (ii) designing a computational framework to recommend teammates to
improve players' performance, and (iii) setting to demonstrate that such
improvements can be predicted via deep learning. We leverage a large dataset
from Dota 2, a popular Multiplayer Online Battle Arena game. We generate a
directed co-play network, whose links' weights depict the effect of teammates
on players' performance. Specifically, we propose a measure of network
influence that captures skill transfer from player to player over time. We then
use such framing to design a recommendation system to suggest new teammates
based on a modified deep neural autoencoder and we demonstrate its
state-of-the-art recommendation performance. We finally provide insights into
skill transfer effects: our experimental results demonstrate that such dynamics
can be predicted using deep neural networks
Provision of Public Goods on Networks: On Existence, Uniqueness, and Centralities
We consider the provision of public goods on networks of strategic agents. We
study different effort outcomes of these network games, namely, the Nash
equilibria, Pareto efficient effort profiles, and semi-cooperative equilibria
(effort profiles resulting from interactions among coalitions of agents). We
identify necessary and sufficient conditions on the structure of the network
for the uniqueness of the Nash equilibrium. We show that our finding unifies
(and strengthens) existing results in the literature. We also identify
conditions for the existence of Nash equilibria for the subclasses of games at
the two extremes of our model, namely games of strategic complements and games
of strategic substitutes. We provide a graph-theoretical interpretation of
agents' efforts at the Nash equilibrium, as well as the Pareto efficient
outcomes and semi-cooperative equilibria, by linking an agent's decision to her
centrality in the interaction network. Using this connection, we separate the
effects of incoming and outgoing edges on agents' efforts and uncover an
alternating effect over walks of different length in the network
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