4,178 research outputs found
Link Clustering with Extended Link Similarity and EQ Evaluation Division.
Link Clustering (LC) is a relatively new method for detecting overlapping communities in networks. The basic principle of LC is to derive a transform matrix whose elements are composed of the link similarity of neighbor links based on the Jaccard distance calculation; then it applies hierarchical clustering to the transform matrix and uses a measure of partition density on the resulting dendrogram to determine the cut level for best community detection. However, the original link clustering method does not consider the link similarity of non-neighbor links, and the partition density tends to divide the communities into many small communities. In this paper, an Extended Link Clustering method (ELC) for overlapping community detection is proposed. The improved method employs a new link similarity, Extended Link Similarity (ELS), to produce a denser transform matrix, and uses the maximum value of EQ (an extended measure of quality of modularity) as a means to optimally cut the dendrogram for better partitioning of the original network space. Since ELS uses more link information, the resulting transform matrix provides a superior basis for clustering and analysis. Further, using the EQ value to find the best level for the hierarchical clustering dendrogram division, we obtain communities that are more sensible and reasonable than the ones obtained by the partition density evaluation. Experimentation on five real-world networks and artificially-generated networks shows that the ELC method achieves higher EQ and In-group Proportion (IGP) values. Additionally, communities are more realistic than those generated by either of the original LC method or the classical CPM method
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
Triangles to Capture Social Cohesion
Although community detection has drawn tremendous amount of attention across
the sciences in the past decades, no formal consensus has been reached on the
very nature of what qualifies a community as such. In this article we take an
orthogonal approach by introducing a novel point of view to the problem of
overlapping communities. Instead of quantifying the quality of a set of
communities, we choose to focus on the intrinsic community-ness of one given
set of nodes. To do so, we propose a general metric on graphs, the cohesion,
based on counting triangles and inspired by well established sociological
considerations. The model has been validated through a large-scale online
experiment called Fellows in which users were able to compute their social
groups on Face- book and rate the quality of the obtained groups. By observing
those ratings in relation to the cohesion we assess that the cohesion is a
strong indicator of users subjective perception of the community-ness of a set
of people
- …