25 research outputs found
Axioms for graph clustering quality functions
We investigate properties that intuitively ought to be satisfied by graph
clustering quality functions, that is, functions that assign a score to a
clustering of a graph. Graph clustering, also known as network community
detection, is often performed by optimizing such a function. Two axioms
tailored for graph clustering quality functions are introduced, and the four
axioms introduced in previous work on distance based clustering are
reformulated and generalized for the graph setting. We show that modularity, a
standard quality function for graph clustering, does not satisfy all of these
six properties. This motivates the derivation of a new family of quality
functions, adaptive scale modularity, which does satisfy the proposed axioms.
Adaptive scale modularity has two parameters, which give greater flexibility in
the kinds of clusterings that can be found. Standard graph clustering quality
functions, such as normalized cut and unnormalized cut, are obtained as special
cases of adaptive scale modularity.
In general, the results of our investigation indicate that the considered
axiomatic framework covers existing `good' quality functions for graph
clustering, and can be used to derive an interesting new family of quality
functions.Comment: 23 pages. Full text and sources available on:
http://www.cs.ru.nl/~T.vanLaarhoven/graph-clustering-axioms-2014
Generative models for local network community detection
Local network community detection aims to find a single community in a large
network, while inspecting only a small part of that network around a given seed
node. This is much cheaper than finding all communities in a network. Most
methods for local community detection are formulated as ad-hoc optimization
problems. In this work, we instead start from a generative model for networks
with community structure. By assuming that the network is uniform, we can
approximate the structure of unobserved parts of the network to obtain a method
for local community detection. We apply this local approximation technique to
two variants of the stochastic block model. To our knowledge, this results in
the first local community detection methods based on probabilistic models.
Interestingly, in the limit, one of the proposed approximations corresponds to
conductance, a popular metric in this field. Experiments on real and synthetic
datasets show comparable or improved results compared to state-of-the-art local
community detection algorithms
Network community detection with edge classifiers trained on LFR graphs
Abstract. Graphs generated using the Lancichinetti-Fortunato-Radicchi (LFR) model are widely used for assessing the performance of network community detection algorithms. This paper investigates an laternative use of LFR graphs: as training data for learning classifiers that discriminate between edges that are ‘within ’ a community and ‘between ’ network communities. The LFR generator has a parameter that controls the extent to which communities are mixed, and hence harder to detect. We show experimentally that a linear edge-wise weighted support vector machine classifier trained on a graph with more mixed communities also works well when tested on easier graph instances, while it achieves mixed performance on real-life networks, with a tendency towards finding many communities.
Unsupervised Domain Adaptation using Graph Transduction Games
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the
unlabeled instances of a dataset from a target domain, using labeled instances
of a dataset from a related source domain. In this paper, we propose to cast
this problem in a game-theoretic setting as a non-cooperative game and
introduce a fully automatized iterative algorithm for UDA based on graph
transduction games (GTG). The main advantages of this approach are its
principled foundation, guaranteed termination of the iterative algorithms to a
Nash equilibrium (which corresponds to a consistent labeling condition) and
soft labels quantifying the uncertainty of the label assignment process. We
also investigate the beneficial effect of using pseudo-labels from linear
classifiers to initialize the iterative process. The performance of the
resulting methods is assessed on publicly available object recognition
benchmark datasets involving both shallow and deep features. Results of
experiments demonstrate the suitability of the proposed game-theoretic approach
for solving UDA tasks.Comment: Oral IJCNN 201