12,077 research outputs found
A New overlapping community detection algorithm based on similarity of neighbors in complex networks
summary:Community detection algorithms help us improve the management of complex networks and provide a clean sight of them. We can encounter complex networks in various fields such as social media, bioinformatics, recommendation systems, and search engines. As the definition of the community changes based on the problem considered, there is no algorithm that works universally for all kinds of data and network structures. Communities can be disjointed such that each member is in at most one community or overlapping such that every member is in at least one community. In this study, we examine the problem of finding overlapping communities in complex networks and propose a new algorithm based on the similarity of neighbors. This algorithm runs in running time in the complex network containing number of relationships. To compare our algorithm with existing ones, we select the most successful four algorithms from the Community Detection library (CDlib) by eliminating the algorithms that require prior knowledge, are unstable, and are time-consuming. We evaluate the successes of the proposed algorithm and the selected algorithms using various known metrics such as modularity, F-score, and Normalized Mutual Information. In addition, we adapt the coverage metric defined for disjoint communities to overlapping communities and also make comparisons with this metric. We also test all of the algorithms on small graphs of real communities. The experimental results show that the proposed algorithm is successful in finding overlapping communities
Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale
Notions of community quality underlie network clustering. While studies
surrounding network clustering are increasingly common, a precise understanding
of the realtionship between different cluster quality metrics is unknown. In
this paper, we examine the relationship between stand-alone cluster quality
metrics and information recovery metrics through a rigorous analysis of four
widely-used network clustering algorithms -- Louvain, Infomap, label
propagation, and smart local moving. We consider the stand-alone quality
metrics of modularity, conductance, and coverage, and we consider the
information recovery metrics of adjusted Rand score, normalized mutual
information, and a variant of normalized mutual information used in previous
work. Our study includes both synthetic graphs and empirical data sets of sizes
varying from 1,000 to 1,000,000 nodes.
We find significant differences among the results of the different cluster
quality metrics. For example, clustering algorithms can return a value of 0.4
out of 1 on modularity but score 0 out of 1 on information recovery. We find
conductance, though imperfect, to be the stand-alone quality metric that best
indicates performance on information recovery metrics. Our study shows that the
variant of normalized mutual information used in previous work cannot be
assumed to differ only slightly from traditional normalized mutual information.
Smart local moving is the best performing algorithm in our study, but
discrepancies between cluster evaluation metrics prevent us from declaring it
absolutely superior. Louvain performed better than Infomap in nearly all the
tests in our study, contradicting the results of previous work in which Infomap
was superior to Louvain. We find that although label propagation performs
poorly when clusters are less clearly defined, it scales efficiently and
accurately to large graphs with well-defined clusters
Discovering Communities of Community Discovery
Discovering communities in complex networks means grouping nodes similar to
each other, to uncover latent information about them. There are hundreds of
different algorithms to solve the community detection task, each with its own
understanding and definition of what a "community" is. Dozens of review works
attempt to order such a diverse landscape -- classifying community discovery
algorithms by the process they employ to detect communities, by their
explicitly stated definition of community, or by their performance on a
standardized task. In this paper, we classify community discovery algorithms
according to a fourth criterion: the similarity of their results. We create an
Algorithm Similarity Network (ASN), whose nodes are the community detection
approaches, connected if they return similar groupings. We then perform
community detection on this network, grouping algorithms that consistently
return the same partitions or overlapping coverage over a span of more than one
thousand synthetic and real world networks. This paper is an attempt to create
a similarity-based classification of community detection algorithms based on
empirical data. It improves over the state of the art by comparing more than
seventy approaches, discovering that the ASN contains well-separated groups,
making it a sensible tool for practitioners, aiding their choice of algorithms
fitting their analytic needs
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
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