11,034 research outputs found
Sketch-based Influence Maximization and Computation: Scaling up with Guarantees
Propagation of contagion through networks is a fundamental process. It is
used to model the spread of information, influence, or a viral infection.
Diffusion patterns can be specified by a probabilistic model, such as
Independent Cascade (IC), or captured by a set of representative traces.
Basic computational problems in the study of diffusion are influence queries
(determining the potency of a specified seed set of nodes) and Influence
Maximization (identifying the most influential seed set of a given size).
Answering each influence query involves many edge traversals, and does not
scale when there are many queries on very large graphs. The gold standard for
Influence Maximization is the greedy algorithm, which iteratively adds to the
seed set a node maximizing the marginal gain in influence. Greedy has a
guaranteed approximation ratio of at least (1-1/e) and actually produces a
sequence of nodes, with each prefix having approximation guarantee with respect
to the same-size optimum. Since Greedy does not scale well beyond a few million
edges, for larger inputs one must currently use either heuristics or
alternative algorithms designed for a pre-specified small seed set size.
We develop a novel sketch-based design for influence computation. Our greedy
Sketch-based Influence Maximization (SKIM) algorithm scales to graphs with
billions of edges, with one to two orders of magnitude speedup over the best
greedy methods. It still has a guaranteed approximation ratio, and in practice
its quality nearly matches that of exact greedy. We also present influence
oracles, which use linear-time preprocessing to generate a small sketch for
each node, allowing the influence of any seed set to be quickly answered from
the sketches of its nodes.Comment: 10 pages, 5 figures. Appeared at the 23rd Conference on Information
and Knowledge Management (CIKM 2014) in Shanghai, Chin
Functional advantages offered by many-body coherences in biochemical systems
Quantum coherence phenomena driven by electronic-vibrational (vibronic)
interactions, are being reported in many pulse (e.g. laser) driven chemical and
biophysical systems. But what systems-level advantage(s) do such many-body
coherences offer to future technologies? We address this question for pulsed
systems of general size N, akin to the LHCII aggregates found in green plants.
We show that external pulses generate vibronic states containing particular
multipartite entanglements, and that such collective vibronic states increase
the excitonic transfer efficiency. The strength of these many-body coherences
and their robustness to decoherence, increase with aggregate size N and do not
require strong electronic-vibrational coupling. The implications for energy and
information transport are discussed.Comment: arXiv admin note: text overlap with arXiv:1706.0776
Pulsed Generation of Quantum Coherences and Non-classicality in Light-Matter Systems
We show that a pulsed stimulus can be used to generate many-body quantum
coherences in light-matter systems of general size. Specifically, we calculate
the exact real-time evolution of a driven, generic out-of-equilibrium system
comprising an arbitrary number N qubits coupled to a global boson field. A
novel form of dynamically-driven quantum coherence emerges for general N and
without having to access the empirically challenging strong-coupling regime.
Its properties depend on the speed of the changes in the stimulus.
Non-classicalities arise within each subsystem that have eluded previous
analyses. Our findings show robustness to losses and noise, and have potential
functional implications at the systems level for a variety of nanosystems,
including collections of N atoms, molecules, spins, or superconducting qubits
in cavities -- and possibly even vibration-enhanced light harvesting processes
in macromolecules.Comment: 9 pages, 4 figure
Provably Improving Expert Predictions with Prediction Sets
Automated decision support systems promise to help human experts solve tasks more efficiently and accurately. However, existing systems typically require experts to understand when to cede agency to the system or when to exercise their own agency. Moreover, if the experts develop a misplaced trust in the system, their performance may worsen. In this work, we lift the above requirement and develop automated decision support systems that, by design, do not require experts to understand when to trust them to provably improve their performance. To this end, we focus on multiclass classification tasks and consider an automated decision support system that, for each data sample, uses a classifier to recommend a subset of labels to a human expert. We first show that, by looking at the design of such a system from the perspective of conformal prediction, we can ensure that the probability that the recommended subset of labels contains the true label matches almost exactly a target probability value. Then, we develop an efficient and near-optimal search method to find the target probability value under which the expert benefits the most from using our system. Experiments on synthetic and real data demonstrate that our system can help the experts make more accurate predictions and is robust to the accuracy of the classifier it relies on
Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks
A typical viral marketing model identifies influential users in a social network to maximize a single product adoption assuming unlimited user attention, campaign budgets, and time. In reality, multiple products need campaigns, users have limited attention, convincing users incurs costs, and advertisers have limited budgets and expect the adoptions to be maximized soon. Facing these user, monetary, and timing constraints, we formulate the problem as a submodular maximization task in a continuous-time diffusion model under the intersection of a matroid and multiple knapsack constraints. We propose a randomized algorithm estimating the user influence in a network ( nodes, edges) to an accuracy of with randomizations and computations. By exploiting the influence estimation algorithm as a subroutine, we develop an adaptive threshold greedy algorithm achieving an approximation factor of the optimal when out of the knapsack constraints are active. Extensive experiments on networks of millions of nodes demonstrate that the proposed algorithms achieve the state-of-the-art in terms of effectiveness and scalability
Distilling Information Reliability and Source Trustworthiness from Digital Traces
Online knowledge repositories typically rely on their users or dedicated
editors to evaluate the reliability of their content. These evaluations can be
viewed as noisy measurements of both information reliability and information
source trustworthiness. Can we leverage these noisy evaluations, often biased,
to distill a robust, unbiased and interpretable measure of both notions?
In this paper, we argue that the temporal traces left by these noisy
evaluations give cues on the reliability of the information and the
trustworthiness of the sources. Then, we propose a temporal point process
modeling framework that links these temporal traces to robust, unbiased and
interpretable notions of information reliability and source trustworthiness.
Furthermore, we develop an efficient convex optimization procedure to learn the
parameters of the model from historical traces. Experiments on real-world data
gathered from Wikipedia and Stack Overflow show that our modeling framework
accurately predicts evaluation events, provides an interpretable measure of
information reliability and source trustworthiness, and yields interesting
insights about real-world events.Comment: Accepted at 26th World Wide Web conference (WWW-17
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