152 research outputs found
Utilitarian Welfare Optimization in the Generalized Vertex Coloring Games: An Implication to Venue Selection in Events Planning
We consider a general class of multi-agent games in networks, namely the
generalized vertex coloring games (G-VCGs), inspired by real-life applications
of the venue selection problem in events planning. Certain utility responding
to the contemporary coloring assignment will be received by each agent under
some particular mechanism, who, striving to maximize his own utility, is
restricted to local information thus self-organizing when choosing another
color. Our focus is on maximizing some utilitarian-looking welfare objective
function concerning the cumulative utilities across the network in a
decentralized fashion. Firstly, we investigate on a special class of the
G-VCGs, namely Identical Preference VCGs (IP-VCGs) which recovers the
rudimentary work by \cite{chaudhuri2008network}. We reveal its convergence even
under a completely greedy policy and completely synchronous settings, with a
stochastic bound on the converging rate provided. Secondly, regarding the
general G-VCGs, a greediness-preserved Metropolis-Hasting based policy is
proposed for each agent to initiate with the limited information and its
optimality under asynchronous settings is proved using theories from the
regular perturbed Markov processes. The policy was also empirically witnessed
to be robust under independently synchronous settings. Thirdly, in the spirit
of ``robust coloring'', we include an expected loss term in our objective
function to balance between the utilities and robustness. An optimal coloring
for this robust welfare optimization would be derived through a second-stage
MH-policy driven algorithm. Simulation experiments are given to showcase the
efficiency of our proposed strategy.Comment: 35 Page
Overlapping and Robust Edge-Colored Clustering in Hypergraphs
A recent trend in data mining has explored (hyper)graph clustering algorithms
for data with categorical relationship types. Such algorithms have applications
in the analysis of social, co-authorship, and protein interaction networks, to
name a few. Many such applications naturally have some overlap between
clusters, a nuance which is missing from current combinatorial models.
Additionally, existing models lack a mechanism for handling noise in datasets.
We address these concerns by generalizing Edge-Colored Clustering, a recent
framework for categorical clustering of hypergraphs. Our generalizations allow
for a budgeted number of either (a) overlapping cluster assignments or (b) node
deletions. For each new model we present a greedy algorithm which approximately
minimizes an edge mistake objective, as well as bicriteria approximations where
the second approximation factor is on the budget. Additionally, we address the
parameterized complexity of each problem, providing FPT algorithms and hardness
results
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