25,878 research outputs found
Node Classification in Uncertain Graphs
In many real applications that use and analyze networked data, the links in
the network graph may be erroneous, or derived from probabilistic techniques.
In such cases, the node classification problem can be challenging, since the
unreliability of the links may affect the final results of the classification
process. If the information about link reliability is not used explicitly, the
classification accuracy in the underlying network may be affected adversely. In
this paper, we focus on situations that require the analysis of the uncertainty
that is present in the graph structure. We study the novel problem of node
classification in uncertain graphs, by treating uncertainty as a first-class
citizen. We propose two techniques based on a Bayes model and automatic
parameter selection, and show that the incorporation of uncertainty in the
classification process as a first-class citizen is beneficial. We
experimentally evaluate the proposed approach using different real data sets,
and study the behavior of the algorithms under different conditions. The
results demonstrate the effectiveness and efficiency of our approach
Graph Summarization
The continuous and rapid growth of highly interconnected datasets, which are
both voluminous and complex, calls for the development of adequate processing
and analytical techniques. One method for condensing and simplifying such
datasets is graph summarization. It denotes a series of application-specific
algorithms designed to transform graphs into more compact representations while
preserving structural patterns, query answers, or specific property
distributions. As this problem is common to several areas studying graph
topologies, different approaches, such as clustering, compression, sampling, or
influence detection, have been proposed, primarily based on statistical and
optimization methods. The focus of our chapter is to pinpoint the main graph
summarization methods, but especially to focus on the most recent approaches
and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie
The Advantage of Evidential Attributes in Social Networks
Nowadays, there are many approaches designed for the task of detecting
communities in social networks. Among them, some methods only consider the
topological graph structure, while others take use of both the graph structure
and the node attributes. In real-world networks, there are many uncertain and
noisy attributes in the graph. In this paper, we will present how we detect
communities in graphs with uncertain attributes in the first step. The
numerical, probabilistic as well as evidential attributes are generated
according to the graph structure. In the second step, some noise will be added
to the attributes. We perform experiments on graphs with different types of
attributes and compare the detection results in terms of the Normalized Mutual
Information (NMI) values. The experimental results show that the clustering
with evidential attributes gives better results comparing to those with
probabilistic and numerical attributes. This illustrates the advantages of
evidential attributes.Comment: 20th International Conference on Information Fusion, Jul 2017, Xi'an,
Chin
XML Matchers: approaches and challenges
Schema Matching, i.e. the process of discovering semantic correspondences
between concepts adopted in different data source schemas, has been a key topic
in Database and Artificial Intelligence research areas for many years. In the
past, it was largely investigated especially for classical database models
(e.g., E/R schemas, relational databases, etc.). However, in the latest years,
the widespread adoption of XML in the most disparate application fields pushed
a growing number of researchers to design XML-specific Schema Matching
approaches, called XML Matchers, aiming at finding semantic matchings between
concepts defined in DTDs and XSDs. XML Matchers do not just take well-known
techniques originally designed for other data models and apply them on
DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical
structure of a DTD/XSD) to improve the performance of the Schema Matching
process. The design of XML Matchers is currently a well-established research
area. The main goal of this paper is to provide a detailed description and
classification of XML Matchers. We first describe to what extent the
specificities of DTDs/XSDs impact on the Schema Matching task. Then we
introduce a template, called XML Matcher Template, that describes the main
components of an XML Matcher, their role and behavior. We illustrate how each
of these components has been implemented in some popular XML Matchers. We
consider our XML Matcher Template as the baseline for objectively comparing
approaches that, at first glance, might appear as unrelated. The introduction
of this template can be useful in the design of future XML Matchers. Finally,
we analyze commercial tools implementing XML Matchers and introduce two
challenging issues strictly related to this topic, namely XML source clustering
and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure
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