128,140 research outputs found
Core Decomposition in Multilayer Networks: Theory, Algorithms, and Applications
Multilayer networks are a powerful paradigm to model complex systems, where
multiple relations occur between the same entities. Despite the keen interest
in a variety of tasks, algorithms, and analyses in this type of network, the
problem of extracting dense subgraphs has remained largely unexplored so far.
In this work we study the problem of core decomposition of a multilayer
network. The multilayer context is much challenging as no total order exists
among multilayer cores; rather, they form a lattice whose size is exponential
in the number of layers. In this setting we devise three algorithms which
differ in the way they visit the core lattice and in their pruning techniques.
We then move a step forward and study the problem of extracting the
inner-most (also known as maximal) cores, i.e., the cores that are not
dominated by any other core in terms of their core index in all the layers.
Inner-most cores are typically orders of magnitude less than all the cores.
Motivated by this, we devise an algorithm that effectively exploits the
maximality property and extracts inner-most cores directly, without first
computing a complete decomposition.
Finally, we showcase the multilayer core-decomposition tool in a variety of
scenarios and problems. We start by considering the problem of densest-subgraph
extraction in multilayer networks. We introduce a definition of multilayer
densest subgraph that trades-off between high density and number of layers in
which the high density holds, and exploit multilayer core decomposition to
approximate this problem with quality guarantees. As further applications, we
show how to utilize multilayer core decomposition to speed-up the extraction of
frequent cross-graph quasi-cliques and to generalize the community-search
problem to the multilayer setting
Integrating E-Commerce and Data Mining: Architecture and Challenges
We show that the e-commerce domain can provide all the right ingredients for
successful data mining and claim that it is a killer domain for data mining. We
describe an integrated architecture, based on our expe-rience at Blue Martini
Software, for supporting this integration. The architecture can dramatically
reduce the pre-processing, cleaning, and data understanding effort often
documented to take 80% of the time in knowledge discovery projects. We
emphasize the need for data collection at the application server layer (not the
web server) in order to support logging of data and metadata that is essential
to the discovery process. We describe the data transformation bridges required
from the transaction processing systems and customer event streams (e.g.,
clickstreams) to the data warehouse. We detail the mining workbench, which
needs to provide multiple views of the data through reporting, data mining
algorithms, visualization, and OLAP. We con-clude with a set of challenges.Comment: KDD workshop: WebKDD 200
Finding Statistically Significant Interactions between Continuous Features
The search for higher-order feature interactions that are statistically
significantly associated with a class variable is of high relevance in fields
such as Genetics or Healthcare, but the combinatorial explosion of the
candidate space makes this problem extremely challenging in terms of
computational efficiency and proper correction for multiple testing. While
recent progress has been made regarding this challenge for binary features, we
here present the first solution for continuous features. We propose an
algorithm which overcomes the combinatorial explosion of the search space of
higher-order interactions by deriving a lower bound on the p-value for each
interaction, which enables us to massively prune interactions that can never
reach significance and to thereby gain more statistical power. In our
experiments, our approach efficiently detects all significant interactions in a
variety of synthetic and real-world datasets.Comment: 13 pages, 5 figures, 2 tables, accepted to the 28th International
Joint Conference on Artificial Intelligence (IJCAI 2019
Data mining technology for the evaluation of web-based teaching and learning systems
Instructional design for Web-based teaching and learning environments causes problems for two reasons. Firstly, virtual forms of teaching and learning result in little or no direct contact between instructor and learner, making the evaluation of course effectiveness difficult. Secondly, the Web as a relatively new teaching and learning medium still requires more research into learning processes with this technology. We propose data mining â techniques to discover and extract knowledge from a database â as a tool to support the analysis of student learning processes and the evaluation of the effectiveness and usability of
Web-based courses. We present and illustrate different data mining techniques for the evaluation of Web-based teaching and learning systems
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