771 research outputs found
The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets
Traditional verbatim browsers give back information in a linear way according
to a ranking performed by a search engine that may not be optimal for the
surfer. The latter may need to assess the pertinence of the information
retrieved, particularly when she wants to explore other facets of a
multi-facetted information space. For instance, in a multimedia dataset
different facets such as keywords, authors, publication category, organisations
and figures can be of interest. The facet simultaneous visualisation can help
to gain insights on the information retrieved and call for further searches.
Facets are co-occurence networks, modeled by HyperBag-Graphs -- families of
multisets -- and are in fact linked not only to the publication itself, but to
any chosen reference. These references allow to navigate inside the dataset and
perform visual queries. We explore here the case of scientific publications
based on Arxiv searches.Comment: Extension of the hypergraph framework shortly presented in
arXiv:1809.00164 (possible small overlaps); use the theoretical framework of
hb-graphs presented in arXiv:1809.0019
Network Capacity Bound for Personalized PageRank in Multimodal Networks
In a former paper the concept of Bipartite PageRank was introduced and a
theorem on the limit of authority flowing between nodes for personalized
PageRank has been generalized. In this paper we want to extend those results to
multimodal networks. In particular we introduce a hypergraph type that may be
used for describing multimodal network where a hyperlink connects nodes from
each of the modalities. We introduce a generalisation of PageRank for such
graphs and define the respective random walk model that can be used for
computations. we finally state and prove theorems on the limit of outflow of
authority for cases where individual modalities have identical and distinct
damping factors.Comment: 28 pages. arXiv admin note: text overlap with arXiv:1702.0373
A Hypergraph Data Model for Expert-Finding in Multimedia Social Networks
Online Social Networks (OSNs) have found widespread applications in every area of our life. A large number of people have signed up to OSN for different purposes, including to meet old friends, to choose a given company, to identify expert users about a given topic, producing a large number of social connections. These aspects have led to the birth of a new generation of OSNs, called Multimedia Social Networks (MSNs), in which user-generated content plays a key role to enable interactions among users. In this work, we propose a novel expert-finding technique exploiting a hypergraph-based data model for MSNs. In particular, some user-ranking measures, obtained considering only particular useful hyperpaths, have been profitably used to evaluate the related expertness degree with respect to a given social topic. Several experiments on Last.FM have been performed to evaluate the proposed approach's effectiveness, encouraging future work in this direction for supporting several applications such as multimedia recommendation, influence analysis, and so on
Structural Deep Embedding for Hyper-Networks
Network embedding has recently attracted lots of attentions in data mining.
Existing network embedding methods mainly focus on networks with pairwise
relationships. In real world, however, the relationships among data points
could go beyond pairwise, i.e., three or more objects are involved in each
relationship represented by a hyperedge, thus forming hyper-networks. These
hyper-networks pose great challenges to existing network embedding methods when
the hyperedges are indecomposable, that is to say, any subset of nodes in a
hyperedge cannot form another hyperedge. These indecomposable hyperedges are
especially common in heterogeneous networks. In this paper, we propose a novel
Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with
indecomposable hyperedges. More specifically, we theoretically prove that any
linear similarity metric in embedding space commonly used in existing methods
cannot maintain the indecomposibility property in hyper-networks, and thus
propose a new deep model to realize a non-linear tuplewise similarity function
while preserving both local and global proximities in the formed embedding
space. We conduct extensive experiments on four different types of
hyper-networks, including a GPS network, an online social network, a drug
network and a semantic network. The empirical results demonstrate that our
method can significantly and consistently outperform the state-of-the-art
algorithms.Comment: Accepted by AAAI 1
HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities
Multimodal datasets contain an enormous amount of relational information,
which grows exponentially with the introduction of new modalities. Learning
representations in such a scenario is inherently complex due to the presence of
multiple heterogeneous information channels. These channels can encode both (a)
inter-relations between the items of different modalities and (b)
intra-relations between the items of the same modality. Encoding multimedia
items into a continuous low-dimensional semantic space such that both types of
relations are captured and preserved is extremely challenging, especially if
the goal is a unified end-to-end learning framework. The two key challenges
that need to be addressed are: 1) the framework must be able to merge complex
intra and inter relations without losing any valuable information and 2) the
learning model should be invariant to the addition of new and potentially very
different modalities. In this paper, we propose a flexible framework which can
scale to data streams from many modalities. To that end we introduce a
hypergraph-based model for data representation and deploy Graph Convolutional
Networks to fuse relational information within and across modalities. Our
approach provides an efficient solution for distributing otherwise extremely
computationally expensive or even unfeasible training processes across
multiple-GPUs, without any sacrifices in accuracy. Moreover, adding new
modalities to our model requires only an additional GPU unit keeping the
computational time unchanged, which brings representation learning to truly
multimodal datasets. We demonstrate the feasibility of our approach in the
experiments on multimedia datasets featuring second, third and fourth order
relations
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