4,320 research outputs found
A semi-supervised approach to visualizing and manipulating overlapping communities
When evaluating a network topology, occasionally data structures cannot be segmented into absolute, heterogeneous groups. There may be a spectrum to the dataset that does not allow for this hard clustering approach and may need to segment using fuzzy/overlapping communities or cliques. Even to this degree, when group members can belong to multiple cliques, there leaves an ever present layer of doubt, noise, and outliers caused by the overlapping clustering algorithms. These imperfections can either be corrected by an expert user to enhance the clustering algorithm or to preserve their own mental models of the communities. Presented is a visualization that models overlapping community membership and provides an interactive interface to facilitate a quick and efficient means of both sorting through large network topologies and preserving the user's mental model of the structure. © 2013 IEEE
The State-of-the-Art of Set Visualization
Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net
Community detection by using the extended modularity
This article is about community detection algorithms in graphs. First a new method will be introduced, which is based on an extension [16] of the commonly used modularity [17, 18, 19, 20] and gives overlapping communities. We list and compare the results given by our new method and some other algorithms yielding either overlapping or non-overlapping communities. While the main use of the proposed algorithm is benchmarking, we also consider the possibility of hot starts, and some further extensions that considers the degree distribution of the graphs
Overlapping Community Detection Optimization and Nash Equilibrium
Community detection using both graphs and social networks is the focus of
many algorithms. Recent methods aimed at optimizing the so-called modularity
function proceed by maximizing relations within communities while minimizing
inter-community relations.
However, given the NP-completeness of the problem, these algorithms are
heuristics that do not guarantee an optimum. In this paper, we introduce a new
algorithm along with a function that takes an approximate solution and modifies
it in order to reach an optimum. This reassignment function is considered a
'potential function' and becomes a necessary condition to asserting that the
computed optimum is indeed a Nash Equilibrium. We also use this function to
simultaneously show partitioning and overlapping communities, two detection and
visualization modes of great value in revealing interesting features of a
social network. Our approach is successfully illustrated through several
experiments on either real unipartite, multipartite or directed graphs of
medium and large-sized datasets.Comment: Submitted to KD
Egomunities, Exploring Socially Cohesive Person-based Communities
In the last few years, there has been a great interest in detecting
overlapping communities in complex networks, which is understood as dense
groups of nodes featuring a low outbound density. To date, most methods used to
compute such communities stem from the field of disjoint community detection by
either extending the concept of modularity to an overlapping context or by
attempting to decompose the whole set of nodes into several possibly
overlapping subsets. In this report we take an orthogonal approach by
introducing a metric, the cohesion, rooted in sociological considerations. The
cohesion quantifies the community-ness of one given set of nodes, based on the
notions of triangles - triplets of connected nodes - and weak ties, instead of
the classical view using only edge density. A set of nodes has a high cohesion
if it features a high density of triangles and intersects few triangles with
the rest of the network. As such, we introduce a numerical characterization of
communities: sets of nodes featuring a high cohesion. We then present a new
approach to the problem of overlapping communities by introducing the concept
of ego-munities, which are subjective communities centered around a given node,
specifically inside its neighborhood. We build upon the cohesion to construct a
heuristic algorithm which outputs a node's ego-munities by attempting to
maximize their cohesion. We illustrate the pertinence of our method with a
detailed description of one person's ego-munities among Facebook friends. We
finally conclude by describing promising applications of ego-munities such as
information inference and interest recommendations, and present a possible
extension to cohesion in the case of weighted networks
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