1,710 research outputs found
Median evidential c-means algorithm and its application to community detection
Median clustering is of great value for partitioning relational data. In this
paper, a new prototype-based clustering method, called Median Evidential
C-Means (MECM), which is an extension of median c-means and median fuzzy
c-means on the theoretical framework of belief functions is proposed. The
median variant relaxes the restriction of a metric space embedding for the
objects but constrains the prototypes to be in the original data set. Due to
these properties, MECM could be applied to graph clustering problems. A
community detection scheme for social networks based on MECM is investigated
and the obtained credal partitions of graphs, which are more refined than crisp
and fuzzy ones, enable us to have a better understanding of the graph
structures. An initial prototype-selection scheme based on evidential
semi-centrality is presented to avoid local premature convergence and an
evidential modularity function is defined to choose the optimal number of
communities. Finally, experiments in synthetic and real data sets illustrate
the performance of MECM and show its difference to other methods
Evidential Communities for Complex Networks
Community detection is of great importance for understand-ing graph structure
in social networks. The communities in real-world networks are often
overlapped, i.e. some nodes may be a member of multiple clusters. How to
uncover the overlapping communities/clusters in a complex network is a general
problem in data mining of network data sets. In this paper, a novel algorithm
to identify overlapping communi-ties in complex networks by a combination of an
evidential modularity function, a spectral mapping method and evidential
c-means clustering is devised. Experimental results indicate that this
detection approach can take advantage of the theory of belief functions, and
preforms good both at detecting community structure and determining the
appropri-ate number of clusters. Moreover, the credal partition obtained by the
proposed method could give us a deeper insight into the graph structure
Modularity functions maximization with nonnegative relaxation facilitates community detection in networks
We show here that the problem of maximizing a family of quantitative
functions, encompassing both the modularity (Q-measure) and modularity density
(D-measure), for community detection can be uniformly understood as a
combinatoric optimization involving the trace of a matrix called modularity
Laplacian. Instead of using traditional spectral relaxation, we apply
additional nonnegative constraint into this graph clustering problem and design
efficient algorithms to optimize the new objective. With the explicit
nonnegative constraint, our solutions are very close to the ideal community
indicator matrix and can directly assign nodes into communities. The
near-orthogonal columns of the solution can be reformulated as the posterior
probability of corresponding node belonging to each community. Therefore, the
proposed method can be exploited to identify the fuzzy or overlapping
communities and thus facilitates the understanding of the intrinsic structure
of networks. Experimental results show that our new algorithm consistently,
sometimes significantly, outperforms the traditional spectral relaxation
approaches
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Multi-objective community detection applied to social and COVID-19 constructed networks
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCommunity Detection plays an integral part in network analysis, as it facilitates understanding the structures and functional characteristics of the network. Communities organize real-world networks into densely connected groups of nodes. This thesis provides a critical analysis of the Community Detection and highlights the main areas including algorithms, evaluation metrics, applications, and datasets in social networks.
After defining the research gap, this thesis proposes two Attribute-Based Label Propagation algorithms that maximizes both Modularity and homogeneity. Homogeneity is considered as an objective function one time, and as a constraint another time. To better capture the homogeneity of real-world networks, a new Penalized Homogeneity degree (PHd) is proposed, that can be easily personalized based on the network characteristics.
For the first time, COVID-19 tracing data are utilized to form two dataset networks: one is based on the virus transition between the world countries. While the second dataset is an attributed network based on the virus transition among the contact-tracing in the Kingdom of Bahrain. This type of networks that is concerned in tracking a disease was not formed based on COVID-19 virus and has never been studied as a community detection problem. The proposed datasets are validated and tested in several experiments. The proposed Penalized Homogeneity measure is personalized and used to evaluate the proposed attributed network.
Extensive experiments and analysis are carried out to evaluate the proposed methods and benchmark the results with other well-known algorithms. The results are compared in terms of Modularity, proposed PHd, and accuracy measures. The proposed methods have achieved maximum performance among other methods, with 26.6% better performance in Modularity, and 33.96% in PHd on the proposed dataset, as well as noteworthy results on benchmarking datasets with improvement in Modularity measures of 7.24%, and 4.96% respectively, and proposed PHd values 27% and 81.9%
A mathematical programming approach to overlapping community detection
We propose a new optimization model to detect overlapping communities in networks. The model elaborates suggestions contained in Zhang et al. (2007), in which overlapping communities were identified through the use of a fuzzy membership function, calculated as the outcome of a mathematical programming problem. In our approach, we retain the idea of using both mathematical programming and fuzzy membership to detect overlapping communities, but we replace the fuzzy objective function proposed there with another one, based on the Newman and Girvan's definition of modularity. Next, we formulate a new mixed-integer linear programming model to calculate optimal overlapping communities. After some computational tests, we provide some evidence that our new proposal can fix some biases of the previous model, that is, its tendency of calculating communities composed of almost all nodes. Conversely, our new model can reveal other structural properties, such as nodes or communities acting as bridges between communities. Finally, as mathematical programming can be used only for moderate size networks due to its computation time, we proposed two heuristic algorithms to solve the largest instances, that compare favourably to other methodologies. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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