7,022 research outputs found
Social Learning over Weakly-Connected Graphs
In this paper, we study diffusion social learning over weakly-connected
graphs. We show that the asymmetric flow of information hinders the learning
abilities of certain agents regardless of their local observations. Under some
circumstances that we clarify in this work, a scenario of total influence (or
"mind-control") arises where a set of influential agents ends up shaping the
beliefs of non-influential agents. We derive useful closed-form expressions
that characterize this influence, and which can be used to motivate design
problems to control it. We provide simulation examples to illustrate the
results.Comment: To appear in 2017 in the IEEE Transactions on Signal and Information
Processing over Network
Topology comparison of Twitter diffusion networks effectively reveals misleading information
In recent years, malicious information had an explosive growth in social
media, with serious social and political backlashes. Recent important studies,
featuring large-scale analyses, have produced deeper knowledge about this
phenomenon, showing that misleading information spreads faster, deeper and more
broadly than factual information on social media, where echo chambers,
algorithmic and human biases play an important role in diffusion networks.
Following these directions, we explore the possibility of classifying news
articles circulating on social media based exclusively on a topological
analysis of their diffusion networks. To this aim we collected a large dataset
of diffusion networks on Twitter pertaining to news articles published on two
distinct classes of sources, namely outlets that convey mainstream, reliable
and objective information and those that fabricate and disseminate various
kinds of misleading articles, including false news intended to harm, satire
intended to make people laugh, click-bait news that may be entirely factual or
rumors that are unproven. We carried out an extensive comparison of these
networks using several alignment-free approaches including basic network
properties, centrality measures distributions, and network distances. We
accordingly evaluated to what extent these techniques allow to discriminate
between the networks associated to the aforementioned news domains. Our results
highlight that the communities of users spreading mainstream news, compared to
those sharing misleading news, tend to shape diffusion networks with subtle yet
systematic differences which might be effectively employed to identify
misleading and harmful information.Comment: A revised new version is available on Scientific Report
Cooperative learning in multi-agent systems from intermittent measurements
Motivated by the problem of tracking a direction in a decentralized way, we
consider the general problem of cooperative learning in multi-agent systems
with time-varying connectivity and intermittent measurements. We propose a
distributed learning protocol capable of learning an unknown vector from
noisy measurements made independently by autonomous nodes. Our protocol is
completely distributed and able to cope with the time-varying, unpredictable,
and noisy nature of inter-agent communication, and intermittent noisy
measurements of . Our main result bounds the learning speed of our
protocol in terms of the size and combinatorial features of the (time-varying)
networks connecting the nodes
Context-Dependent Diffusion Network for Visual Relationship Detection
Visual relationship detection can bridge the gap between computer vision and
natural language for scene understanding of images. Different from pure object
recognition tasks, the relation triplets of subject-predicate-object lie on an
extreme diversity space, such as \textit{person-behind-person} and
\textit{car-behind-building}, while suffering from the problem of combinatorial
explosion. In this paper, we propose a context-dependent diffusion network
(CDDN) framework to deal with visual relationship detection. To capture the
interactions of different object instances, two types of graphs, word semantic
graph and visual scene graph, are constructed to encode global context
interdependency. The semantic graph is built through language priors to model
semantic correlations across objects, whilst the visual scene graph defines the
connections of scene objects so as to utilize the surrounding scene
information. For the graph-structured data, we design a diffusion network to
adaptively aggregate information from contexts, which can effectively learn
latent representations of visual relationships and well cater to visual
relationship detection in view of its isomorphic invariance to graphs.
Experiments on two widely-used datasets demonstrate that our proposed method is
more effective and achieves the state-of-the-art performance.Comment: 8 pages, 3 figures, 2018 ACM Multimedia Conference (MM'18
Euclidean Distances, soft and spectral Clustering on Weighted Graphs
We define a class of Euclidean distances on weighted graphs, enabling to
perform thermodynamic soft graph clustering. The class can be constructed form
the "raw coordinates" encountered in spectral clustering, and can be extended
by means of higher-dimensional embeddings (Schoenberg transformations).
Geographical flow data, properly conditioned, illustrate the procedure as well
as visualization aspects.Comment: accepted for presentation (and further publication) at the ECML PKDD
2010 conferenc
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