61,005 research outputs found
A New Information-Theoretical Distance Measure for Evaluating Community Detection Algorithms
Community detection is a research area from network science dealing withthe investigation of complex networks such as social or biological networks, aimingto identify subgroups (communities) of entities (nodes) thatare more closely relatedto each other inside the community than with the remaining entities in the network.Various community detection algorithms have been developed and used in the literaturehowever evaluating community structures that have been automatically detected isa challenging task due to varying results in different scenarios.Current evaluationmeasures that compare extracted community structures with the reference structure orground truth suffer from various drawbacks; some of them having beenpoint out in theliterature. Information theoretic measures form a fundamental classin this domain andhave recently received increasing interest. However even the well employed measures(NVI and NID) also share some limitations, particularly they are biased toward thenumber of communities in the network. The main contribution ofthis paper is tointroduce a new measure that overcomes this limitation while holding the importantproperties of measures. We review the mathematical properties of our measure based onÂż2divergence inspired fromf-divergence measures in information theory. Theoreticalproperties as well as experimental results in various scenarios show the superiority of theproposed measure to evaluate community detection over the ones from the literature
Network Community Detection on Metric Space
Community detection in a complex network is an important problem of much
interest in recent years. In general, a community detection algorithm chooses
an objective function and captures the communities of the network by optimizing
the objective function, and then, one uses various heuristics to solve the
optimization problem to extract the interesting communities for the user. In
this article, we demonstrate the procedure to transform a graph into points of
a metric space and develop the methods of community detection with the help of
a metric defined for a pair of points. We have also studied and analyzed the
community structure of the network therein. The results obtained with our
approach are very competitive with most of the well-known algorithms in the
literature, and this is justified over the large collection of datasets. On the
other hand, it can be observed that time taken by our algorithm is quite less
compared to other methods and justifies the theoretical findings
Comparative Evaluation of Community Detection Algorithms: A Topological Approach
Community detection is one of the most active fields in complex networks
analysis, due to its potential value in practical applications. Many works
inspired by different paradigms are devoted to the development of algorithmic
solutions allowing to reveal the network structure in such cohesive subgroups.
Comparative studies reported in the literature usually rely on a performance
measure considering the community structure as a partition (Rand Index,
Normalized Mutual information, etc.). However, this type of comparison neglects
the topological properties of the communities. In this article, we present a
comprehensive comparative study of a representative set of community detection
methods, in which we adopt both types of evaluation. Community-oriented
topological measures are used to qualify the communities and evaluate their
deviation from the reference structure. In order to mimic real-world systems,
we use artificially generated realistic networks. It turns out there is no
equivalence between both approaches: a high performance does not necessarily
correspond to correct topological properties, and vice-versa. They can
therefore be considered as complementary, and we recommend applying both of
them in order to perform a complete and accurate assessment
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
- …