8,677 research outputs found
Molecular Model of Dynamic Social Network Based on E-mail communication
In this work we consider an application of physically inspired sociodynamical model to the modelling of the evolution of email-based social network. Contrary to the standard approach of sociodynamics, which assumes expressing of system dynamics with heuristically defined simple rules, we postulate the inference of these rules from the real data and their application within a dynamic molecular model. We present how to embed the n-dimensional social space in Euclidean one. Then, inspired by the Lennard-Jones potential, we define a data-driven social potential function and apply the resultant force to a real e-mail communication network in a course of a molecular simulation, with network nodes taking on the role of interacting particles. We discuss all steps of the modelling process, from data preparation, through embedding and the molecular simulation itself, to transformation from the embedding space back to a graph structure. The conclusions, drawn from examining the resultant networks in stable, minimum-energy states, emphasize the role of the embedding process projecting the non–metric social graph into the Euclidean space, the significance of the unavoidable loss of information connected with this procedure and the resultant preservation of global rather than local properties of the initial network. We also argue applicability of our method to some classes of problems, while also signalling the areas which require further research in order to expand this applicability domain
Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering
Deep metric learning has been widely applied in many computer vision tasks,
and recently, it is more attractive in \emph{zero-shot image retrieval and
clustering}(ZSRC) where a good embedding is requested such that the unseen
classes can be distinguished well. Most existing works deem this 'good'
embedding just to be the discriminative one and thus race to devise powerful
metric objectives or hard-sample mining strategies for leaning discriminative
embedding. However, in this paper, we first emphasize that the generalization
ability is a core ingredient of this 'good' embedding as well and largely
affects the metric performance in zero-shot settings as a matter of fact. Then,
we propose the Energy Confused Adversarial Metric Learning(ECAML) framework to
explicitly optimize a robust metric. It is mainly achieved by introducing an
interesting Energy Confusion regularization term, which daringly breaks away
from the traditional metric learning idea of discriminative objective devising,
and seeks to 'confuse' the learned model so as to encourage its generalization
ability by reducing overfitting on the seen classes. We train this confusion
term together with the conventional metric objective in an adversarial manner.
Although it seems weird to 'confuse' the network, we show that our ECAML indeed
serves as an efficient regularization technique for metric learning and is
applicable to various conventional metric methods. This paper empirically and
experimentally demonstrates the importance of learning embedding with good
generalization, achieving state-of-the-art performances on the popular CUB,
CARS, Stanford Online Products and In-Shop datasets for ZSRC tasks.
\textcolor[rgb]{1, 0, 0}{Code available at http://www.bhchen.cn/}.Comment: AAAI 2019, Spotligh
DeepInf: Social Influence Prediction with Deep Learning
Social and information networking activities such as on Facebook, Twitter,
WeChat, and Weibo have become an indispensable part of our everyday life, where
we can easily access friends' behaviors and are in turn influenced by them.
Consequently, an effective social influence prediction for each user is
critical for a variety of applications such as online recommendation and
advertising.
Conventional social influence prediction approaches typically design various
hand-crafted rules to extract user- and network-specific features. However,
their effectiveness heavily relies on the knowledge of domain experts. As a
result, it is usually difficult to generalize them into different domains.
Inspired by the recent success of deep neural networks in a wide range of
computing applications, we design an end-to-end framework, DeepInf, to learn
users' latent feature representation for predicting social influence. In
general, DeepInf takes a user's local network as the input to a graph neural
network for learning her latent social representation. We design strategies to
incorporate both network structures and user-specific features into
convolutional neural and attention networks. Extensive experiments on Open
Academic Graph, Twitter, Weibo, and Digg, representing different types of
social and information networks, demonstrate that the proposed end-to-end
model, DeepInf, significantly outperforms traditional feature engineering-based
approaches, suggesting the effectiveness of representation learning for social
applications.Comment: 10 pages, 5 figures, to appear in KDD 2018 proceeding
Navigation of brain networks
Understanding the mechanisms of neural communication in large-scale brain
networks remains a major goal in neuroscience. We investigated whether
navigation is a parsimonious routing model for connectomics. Navigating a
network involves progressing to the next node that is closest in distance to a
desired destination. We developed a measure to quantify navigation efficiency
and found that connectomes in a range of mammalian species (human, mouse and
macaque) can be successfully navigated with near-optimal efficiency (>80% of
optimal efficiency for typical connection densities). Rewiring network topology
or repositioning network nodes resulted in 45%-60% reductions in navigation
performance. Specifically, we found that brain networks cannot be progressively
rewired (randomized or clusterized) to result in topologies with significantly
improved navigation performance. Navigation was also found to: i) promote a
resource-efficient distribution of the information traffic load, potentially
relieving communication bottlenecks; and, ii) explain significant variation in
functional connectivity. Unlike prevalently studied communication strategies in
connectomics, navigation does not mandate biologically unrealistic assumptions
about global knowledge of network topology. We conclude that the wiring and
spatial embedding of brain networks is conducive to effective decentralized
communication. Graph-theoretic studies of the connectome should consider
measures of network efficiency and centrality that are consistent with
decentralized models of neural communication
Socially-Aware Distributed Hash Tables for Decentralized Online Social Networks
Many decentralized online social networks (DOSNs) have been proposed due to
an increase in awareness related to privacy and scalability issues in
centralized social networks. Such decentralized networks transfer processing
and storage functionalities from the service providers towards the end users.
DOSNs require individualistic implementation for services, (i.e., search,
information dissemination, storage, and publish/subscribe). However, many of
these services mostly perform social queries, where OSN users are interested in
accessing information of their friends. In our work, we design a socially-aware
distributed hash table (DHTs) for efficient implementation of DOSNs. In
particular, we propose a gossip-based algorithm to place users in a DHT, while
maximizing the social awareness among them. Through a set of experiments, we
show that our approach reduces the lookup latency by almost 30% and improves
the reliability of the communication by nearly 10% via trusted contacts.Comment: 10 pages, p2p 2015 conferenc
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