191,712 research outputs found
Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss
Several natural phenomena and complex systems are often represented as
networks. Discovering their community structure is a fundamental task for
understanding these networks. Many algorithms have been proposed, but recently,
Graph Neural Networks (GNN) have emerged as a compelling approach for enhancing
this task.In this paper, we introduce a simple, efficient, and
clustering-oriented model based on unsupervised \textbf{G}raph Attention
\textbf{A}uto\textbf{E}ncoder for community detection in attributed networks
(GAECO). The proposed model adeptly learns representations from both the
network's topology and attribute information, simultaneously addressing dual
objectives: reconstruction and community discovery. It places a particular
emphasis on discovering compact communities by robustly minimizing clustering
errors. The model employs k-means as an objective function and utilizes a
multi-head Graph Attention Auto-Encoder for decoding the representations.
Experiments conducted on three datasets of attributed networks show that our
method surpasses state-of-the-art algorithms in terms of NMI and ARI.
Additionally, our approach scales effectively with the size of the network,
making it suitable for large-scale applications. The implications of our
findings extend beyond biological network interpretation and social network
analysis, where knowledge of the fundamental community structure is essential.Comment: 7 pages, 5 Figure
Bi-Objective Community Detection (BOCD) in Networks using Genetic Algorithm
A lot of research effort has been put into community detection from all
corners of academic interest such as physics, mathematics and computer science.
In this paper I have proposed a Bi-Objective Genetic Algorithm for community
detection which maximizes modularity and community score. Then the results
obtained for both benchmark and real life data sets are compared with other
algorithms using the modularity and MNI performance metrics. The results show
that the BOCD algorithm is capable of successfully detecting community
structure in both real life and synthetic datasets, as well as improving upon
the performance of previous techniques.Comment: 11 pages, 3 Figures, 3 Tables. arXiv admin note: substantial text
overlap with arXiv:0906.061
A similarity-based community detection method with multiple prototype representation
Communities are of great importance for understanding graph structures in
social networks. Some existing community detection algorithms use a single
prototype to represent each group. In real applications, this may not
adequately model the different types of communities and hence limits the
clustering performance on social networks. To address this problem, a
Similarity-based Multi-Prototype (SMP) community detection approach is proposed
in this paper. In SMP, vertices in each community carry various weights to
describe their degree of representativeness. This mechanism enables each
community to be represented by more than one node. The centrality of nodes is
used to calculate prototype weights, while similarity is utilized to guide us
to partitioning the graph. Experimental results on computer generated and
real-world networks clearly show that SMP performs well for detecting
communities. Moreover, the method could provide richer information for the
inner structure of the detected communities with the help of prototype weights
compared with the existing community detection models
Different approaches to community detection
A precise definition of what constitutes a community in networks has remained
elusive. Consequently, network scientists have compared community detection
algorithms on benchmark networks with a particular form of community structure
and classified them based on the mathematical techniques they employ. However,
this comparison can be misleading because apparent similarities in their
mathematical machinery can disguise different reasons for why we would want to
employ community detection in the first place. Here we provide a focused review
of these different motivations that underpin community detection. This
problem-driven classification is useful in applied network science, where it is
important to select an appropriate algorithm for the given purpose. Moreover,
highlighting the different approaches to community detection also delineates
the many lines of research and points out open directions and avenues for
future research.Comment: 14 pages, 2 figures. Written as a chapter for forthcoming Advances in
network clustering and blockmodeling, and based on an extended version of The
many facets of community detection in complex networks, Appl. Netw. Sci. 2: 4
(2017) by the same author
Feature learning in feature-sample networks using multi-objective optimization
Data and knowledge representation are fundamental concepts in machine
learning. The quality of the representation impacts the performance of the
learning model directly. Feature learning transforms or enhances raw data to
structures that are effectively exploited by those models. In recent years,
several works have been using complex networks for data representation and
analysis. However, no feature learning method has been proposed for such
category of techniques. Here, we present an unsupervised feature learning
mechanism that works on datasets with binary features. First, the dataset is
mapped into a feature--sample network. Then, a multi-objective optimization
process selects a set of new vertices to produce an enhanced version of the
network. The new features depend on a nonlinear function of a combination of
preexisting features. Effectively, the process projects the input data into a
higher-dimensional space. To solve the optimization problem, we design two
metaheuristics based on the lexicographic genetic algorithm and the improved
strength Pareto evolutionary algorithm (SPEA2). We show that the enhanced
network contains more information and can be exploited to improve the
performance of machine learning methods. The advantages and disadvantages of
each optimization strategy are discussed.Comment: 7 pages, 4 figure
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