1,386 research outputs found
K–partitioning of Signed or Weighted Bipartite Graphs
Abstract — In this work, K-partitioning of signed or weighted bipartite graph problem has been introduced, which appears as a real life problem where the partitions of bipartite graph represent two different entities and the edges between the nodes of the partitions represent the relationships among them. A typical example is the set of people and their opinions, whose strength is represented as signed numerical values. Using the weights on the edges, these bipartite graphs can be partitioned into two or more clusters. In political domain, a cluster represents strong relationship among a group of people and a group of issues. In the paper, we formally define the problem and compare different heuristics, and show through both real and simulated data the effectiveness of our approaches
Unifying Sparsest Cut, Cluster Deletion, and Modularity Clustering Objectives with Correlation Clustering
Graph clustering, or community detection, is the task of identifying groups
of closely related objects in a large network. In this paper we introduce a new
community-detection framework called LambdaCC that is based on a specially
weighted version of correlation clustering. A key component in our methodology
is a clustering resolution parameter, , which implicitly controls the
size and structure of clusters formed by our framework. We show that, by
increasing this parameter, our objective effectively interpolates between two
different strategies in graph clustering: finding a sparse cut and forming
dense subgraphs. Our methodology unifies and generalizes a number of other
important clustering quality functions including modularity, sparsest cut, and
cluster deletion, and places them all within the context of an optimization
problem that has been well studied from the perspective of approximation
algorithms. Our approach is particularly relevant in the regime of finding
dense clusters, as it leads to a 2-approximation for the cluster deletion
problem. We use our approach to cluster several graphs, including large
collaboration networks and social networks
Clustering and Community Detection in Directed Networks: A Survey
Networks (or graphs) appear as dominant structures in diverse domains,
including sociology, biology, neuroscience and computer science. In most of the
aforementioned cases graphs are directed - in the sense that there is
directionality on the edges, making the semantics of the edges non symmetric.
An interesting feature that real networks present is the clustering or
community structure property, under which the graph topology is organized into
modules commonly called communities or clusters. The essence here is that nodes
of the same community are highly similar while on the contrary, nodes across
communities present low similarity. Revealing the underlying community
structure of directed complex networks has become a crucial and
interdisciplinary topic with a plethora of applications. Therefore, naturally
there is a recent wealth of research production in the area of mining directed
graphs - with clustering being the primary method and tool for community
detection and evaluation. The goal of this paper is to offer an in-depth review
of the methods presented so far for clustering directed networks along with the
relevant necessary methodological background and also related applications. The
survey commences by offering a concise review of the fundamental concepts and
methodological base on which graph clustering algorithms capitalize on. Then we
present the relevant work along two orthogonal classifications. The first one
is mostly concerned with the methodological principles of the clustering
algorithms, while the second one approaches the methods from the viewpoint
regarding the properties of a good cluster in a directed network. Further, we
present methods and metrics for evaluating graph clustering results,
demonstrate interesting application domains and provide promising future
research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear
Clique Graphs and Overlapping Communities
It is shown how to construct a clique graph in which properties of cliques of
a fixed order in a given graph are represented by vertices in a weighted graph.
Various definitions and motivations for these weights are given. The detection
of communities or clusters is used to illustrate how a clique graph may be
exploited. In particular a benchmark network is shown where clique graphs find
the overlapping communities accurately while vertex partition methods fail.Comment: 23 pages plus 16 additional pages in appendice
Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
Network science is an interdisciplinary endeavor, with methods and
applications drawn from across the natural, social, and information sciences. A
prominent problem in network science is the algorithmic detection of
tightly-connected groups of nodes known as communities. We developed a
generalized framework of network quality functions that allowed us to study the
community structure of arbitrary multislice networks, which are combinations of
individual networks coupled through links that connect each node in one network
slice to itself in other slices. This framework allows one to study community
structure in a very general setting encompassing networks that evolve over
time, have multiple types of links (multiplexity), and have multiple scales.Comment: 31 pages, 3 figures, 1 table. Includes main text and supporting
material. This is the accepted version of the manuscript (the definitive
version appeared in Science), with typographical corrections included her
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