15,894 research outputs found
Graphs in machine learning: an introduction
Graphs are commonly used to characterise interactions between objects of
interest. Because they are based on a straightforward formalism, they are used
in many scientific fields from computer science to historical sciences. In this
paper, we give an introduction to some methods relying on graphs for learning.
This includes both unsupervised and supervised methods. Unsupervised learning
algorithms usually aim at visualising graphs in latent spaces and/or clustering
the nodes. Both focus on extracting knowledge from graph topologies. While most
existing techniques are only applicable to static graphs, where edges do not
evolve through time, recent developments have shown that they could be extended
to deal with evolving networks. In a supervised context, one generally aims at
inferring labels or numerical values attached to nodes using both the graph
and, when they are available, node characteristics. Balancing the two sources
of information can be challenging, especially as they can disagree locally or
globally. In both contexts, supervised and un-supervised, data can be
relational (augmented with one or several global graphs) as described above, or
graph valued. In this latter case, each object of interest is given as a full
graph (possibly completed by other characteristics). In this context, natural
tasks include graph clustering (as in producing clusters of graphs rather than
clusters of nodes in a single graph), graph classification, etc. 1 Real
networks One of the first practical studies on graphs can be dated back to the
original work of Moreno [51] in the 30s. Since then, there has been a growing
interest in graph analysis associated with strong developments in the modelling
and the processing of these data. Graphs are now used in many scientific
fields. In Biology [54, 2, 7], for instance, metabolic networks can describe
pathways of biochemical reactions [41], while in social sciences networks are
used to represent relation ties between actors [66, 56, 36, 34]. Other examples
include powergrids [71] and the web [75]. Recently, networks have also been
considered in other areas such as geography [22] and history [59, 39]. In
machine learning, networks are seen as powerful tools to model problems in
order to extract information from data and for prediction purposes. This is the
object of this paper. For more complete surveys, we refer to [28, 62, 49, 45].
In this section, we introduce notations and highlight properties shared by most
real networks. In Section 2, we then consider methods aiming at extracting
information from a unique network. We will particularly focus on clustering
methods where the goal is to find clusters of vertices. Finally, in Section 3,
techniques that take a series of networks into account, where each network i
Topological Feature Based Classification
There has been a lot of interest in developing algorithms to extract clusters
or communities from networks. This work proposes a method, based on
blockmodelling, for leveraging communities and other topological features for
use in a predictive classification task. Motivated by the issues faced by the
field of community detection and inspired by recent advances in Bayesian topic
modelling, the presented model automatically discovers topological features
relevant to a given classification task. In this way, rather than attempting to
identify some universal best set of clusters for an undefined goal, the aim is
to find the best set of clusters for a particular purpose.
Using this method, topological features can be validated and assessed within
a given context by their predictive performance.
The proposed model differs from other relational and semi-supervised learning
models as it identifies topological features to explain the classification
decision. In a demonstration on a number of real networks the predictive
capability of the topological features are shown to rival the performance of
content based relational learners. Additionally, the model is shown to
outperform graph-based semi-supervised methods on directed and approximately
bipartite networks.Comment: Awarded 3rd Best Student Paper at 14th International Conference on
Information Fusion 201
Model Selection in Overlapping Stochastic Block Models
Networks are a commonly used mathematical model to describe the rich set of
interactions between objects of interest. Many clustering methods have been
developed in order to partition such structures, among which several rely on
underlying probabilistic models, typically mixture models. The relevant hidden
structure may however show overlapping groups in several applications. The
Overlapping Stochastic Block Model (2011) has been developed to take this
phenomenon into account. Nevertheless, the problem of the choice of the number
of classes in the inference step is still open. To tackle this issue, we
consider the proposed model in a Bayesian framework and develop a new criterion
based on a non asymptotic approximation of the marginal log-likelihood. We
describe how the criterion can be computed through a variational Bayes EM
algorithm, and demonstrate its efficiency by running it on both simulated and
real data.Comment: articl
Clustering based on Random Graph Model embedding Vertex Features
Large datasets with interactions between objects are common to numerous
scientific fields (i.e. social science, internet, biology...). The interactions
naturally define a graph and a common way to explore or summarize such dataset
is graph clustering. Most techniques for clustering graph vertices just use the
topology of connections ignoring informations in the vertices features. In this
paper, we provide a clustering algorithm exploiting both types of data based on
a statistical model with latent structure characterizing each vertex both by a
vector of features as well as by its connectivity. We perform simulations to
compare our algorithm with existing approaches, and also evaluate our method
with real datasets based on hyper-textual documents. We find that our algorithm
successfully exploits whatever information is found both in the connectivity
pattern and in the features
Dynamic Bayesian Combination of Multiple Imperfect Classifiers
Classifier combination methods need to make best use of the outputs of
multiple, imperfect classifiers to enable higher accuracy classifications. In
many situations, such as when human decisions need to be combined, the base
decisions can vary enormously in reliability. A Bayesian approach to such
uncertain combination allows us to infer the differences in performance between
individuals and to incorporate any available prior knowledge about their
abilities when training data is sparse. In this paper we explore Bayesian
classifier combination, using the computationally efficient framework of
variational Bayesian inference. We apply the approach to real data from a large
citizen science project, Galaxy Zoo Supernovae, and show that our method far
outperforms other established approaches to imperfect decision combination. We
go on to analyse the putative community structure of the decision makers, based
on their inferred decision making strategies, and show that natural groupings
are formed. Finally we present a dynamic Bayesian classifier combination
approach and investigate the changes in base classifier performance over time.Comment: 35 pages, 12 figure
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