870 research outputs found
Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach
Influence Maximization is an extensively-studied problem that targets at
selecting a set of initial seed nodes in the Online Social Networks (OSNs) to
spread the influence as widely as possible. However, it remains an open
challenge to design fast and accurate algorithms to find solutions in
large-scale OSNs. Prior Monte-Carlo-simulation-based methods are slow and not
scalable, while other heuristic algorithms do not have any theoretical
guarantee and they have been shown to produce poor solutions for quite some
cases. In this paper, we propose hop-based algorithms that can easily scale to
millions of nodes and billions of edges. Unlike previous heuristics, our
proposed hop-based approaches can provide certain theoretical guarantees.
Experimental evaluations with real OSN datasets demonstrate the efficiency and
effectiveness of our algorithms.Comment: Extended version of the conference paper at ASONAM 2017, 11 page
Stability of Influence Maximization
The present article serves as an erratum to our paper of the same title,
which was presented and published in the KDD 2014 conference. In that article,
we claimed falsely that the objective function defined in Section 1.4 is
non-monotone submodular. We are deeply indebted to Debmalya Mandal, Jean
Pouget-Abadie and Yaron Singer for bringing to our attention a counter-example
to that claim.
Subsequent to becoming aware of the counter-example, we have shown that the
objective function is in fact NP-hard to approximate to within a factor of
for any .
In an attempt to fix the record, the present article combines the problem
motivation, models, and experimental results sections from the original
incorrect article with the new hardness result. We would like readers to only
cite and use this version (which will remain an unpublished note) instead of
the incorrect conference version.Comment: Erratum of Paper "Stability of Influence Maximization" which was
presented and published in the KDD1
Fault Tolerance in Cellular Automata at High Fault Rates
A commonly used model for fault-tolerant computation is that of cellular
automata. The essential difficulty of fault-tolerant computation is present in
the special case of simply remembering a bit in the presence of faults, and
that is the case we treat in this paper. We are concerned with the degree (the
number of neighboring cells on which the state transition function depends)
needed to achieve fault tolerance when the fault rate is high (nearly 1/2). We
consider both the traditional transient fault model (where faults occur
independently in time and space) and a recently introduced combined fault model
which also includes manufacturing faults (which occur independently in space,
but which affect cells for all time). We also consider both a purely
probabilistic fault model (in which the states of cells are perturbed at
exactly the fault rate) and an adversarial model (in which the occurrence of a
fault gives control of the state to an omniscient adversary). We show that
there are cellular automata that can tolerate a fault rate (with
) with degree , even with adversarial combined
faults. The simplest such automata are based on infinite regular trees, but our
results also apply to other structures (such as hyperbolic tessellations) that
contain infinite regular trees. We also obtain a lower bound of
, even with purely probabilistic transient faults only
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