4,759 research outputs found
An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
We propose a multi-agent algorithm able to automatically discover relevant
regularities in a given dataset, determining at the same time the set of
configurations of the adopted parametric dissimilarity measure yielding compact
and separated clusters. Each agent operates independently by performing a
Markovian random walk on a suitable weighted graph representation of the input
dataset. Such a weighted graph representation is induced by the specific
parameter configuration of the dissimilarity measure adopted by the agent,
which searches and takes decisions autonomously for one cluster at a time.
Results show that the algorithm is able to discover parameter configurations
that yield a consistent and interpretable collection of clusters. Moreover, we
demonstrate that our algorithm shows comparable performances with other similar
state-of-the-art algorithms when facing specific clustering problems
Hearing the clusters in a graph: A distributed algorithm
We propose a novel distributed algorithm to cluster graphs. The algorithm
recovers the solution obtained from spectral clustering without the need for
expensive eigenvalue/vector computations. We prove that, by propagating waves
through the graph, a local fast Fourier transform yields the local component of
every eigenvector of the Laplacian matrix, thus providing clustering
information. For large graphs, the proposed algorithm is orders of magnitude
faster than random walk based approaches. We prove the equivalence of the
proposed algorithm to spectral clustering and derive convergence rates. We
demonstrate the benefit of using this decentralized clustering algorithm for
community detection in social graphs, accelerating distributed estimation in
sensor networks and efficient computation of distributed multi-agent search
strategies
Complex network classification using partially self-avoiding deterministic walks
Complex networks have attracted increasing interest from various fields of
science. It has been demonstrated that each complex network model presents
specific topological structures which characterize its connectivity and
dynamics. Complex network classification rely on the use of representative
measurements that model topological structures. Although there are a large
number of measurements, most of them are correlated. To overcome this
limitation, this paper presents a new measurement for complex network
classification based on partially self-avoiding walks. We validate the
measurement on a data set composed by 40.000 complex networks of four
well-known models. Our results indicate that the proposed measurement improves
correct classification of networks compared to the traditional ones
Principled Multilayer Network Embedding
Multilayer network analysis has become a vital tool for understanding
different relationships and their interactions in a complex system, where each
layer in a multilayer network depicts the topological structure of a group of
nodes corresponding to a particular relationship. The interactions among
different layers imply how the interplay of different relations on the topology
of each layer. For a single-layer network, network embedding methods have been
proposed to project the nodes in a network into a continuous vector space with
a relatively small number of dimensions, where the space embeds the social
representations among nodes. These algorithms have been proved to have a better
performance on a variety of regular graph analysis tasks, such as link
prediction, or multi-label classification. In this paper, by extending a
standard graph mining into multilayer network, we have proposed three methods
("network aggregation," "results aggregation" and "layer co-analysis") to
project a multilayer network into a continuous vector space. From the
evaluation, we have proved that comparing with regular link prediction methods,
"layer co-analysis" achieved the best performance on most of the datasets,
while "network aggregation" and "results aggregation" also have better
performance than regular link prediction methods
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