292,288 research outputs found
Ranking influential spreaders is an ill-defined problem
Finding influential spreaders of information and disease in networks is an
important theoretical problem, and one of considerable recent interest. It has
been almost exclusively formulated as a node-ranking problem -- methods for
identifying influential spreaders rank nodes according to how influential they
are. In this work, we show that the ranking approach does not necessarily work:
the set of most influential nodes depends on the number of nodes in the set.
Therefore, the set of most important nodes to vaccinate does not need to
have any node in common with the set of most important nodes. We propose
a method for quantifying the extent and impact of this phenomenon, and show
that it is common in both empirical and model networks
Predicting link directions via a recursive subgraph-based ranking
Link directions are essential to the functionality of networks and their
prediction is helpful towards a better knowledge of directed networks from
incomplete real-world data. We study the problem of predicting the directions
of some links by using the existence and directions of the rest of links. We
propose a solution by first ranking nodes in a specific order and then
predicting each link as stemming from a lower-ranked node towards a
higher-ranked one. The proposed ranking method works recursively by utilizing
local indicators on multiple scales, each corresponding to a subgraph extracted
from the original network. Experiments on real networks show that the
directions of a substantial fraction of links can be correctly recovered by our
method, which outperforms either purely local or global methods.Comment: 6 pages, 5 figures; revised arguments for methods section; figures
replotted; minor revision
DeepBox: Learning Objectness with Convolutional Networks
Existing object proposal approaches use primarily bottom-up cues to rank
proposals, while we believe that objectness is in fact a high level construct.
We argue for a data-driven, semantic approach for ranking object proposals. Our
framework, which we call DeepBox, uses convolutional neural networks (CNNs) to
rerank proposals from a bottom-up method. We use a novel four-layer CNN
architecture that is as good as much larger networks on the task of evaluating
objectness while being much faster. We show that DeepBox significantly improves
over the bottom-up ranking, achieving the same recall with 500 proposals as
achieved by bottom-up methods with 2000. This improvement generalizes to
categories the CNN has never seen before and leads to a 4.5-point gain in
detection mAP. Our implementation achieves this performance while running at
260 ms per image.Comment: ICCV 2015 Camera-ready versio
Deep Multi-view Learning to Rank
We study the problem of learning to rank from multiple information sources.
Though multi-view learning and learning to rank have been studied extensively
leading to a wide range of applications, multi-view learning to rank as a
synergy of both topics has received little attention. The aim of the paper is
to propose a composite ranking method while keeping a close correlation with
the individual rankings simultaneously. We present a generic framework for
multi-view subspace learning to rank (MvSL2R), and two novel solutions are
introduced under the framework. The first solution captures information of
feature mappings from within each view as well as across views using
autoencoder-like networks. Novel feature embedding methods are formulated in
the optimization of multi-view unsupervised and discriminant autoencoders.
Moreover, we introduce an end-to-end solution to learning towards both the
joint ranking objective and the individual rankings. The proposed solution
enhances the joint ranking with minimum view-specific ranking loss, so that it
can achieve the maximum global view agreements in a single optimization
process. The proposed method is evaluated on three different ranking problems,
i.e. university ranking, multi-view lingual text ranking and image data
ranking, providing superior results compared to related methods.Comment: Published at IEEE TKD
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