17,760 research outputs found
A framework for community detection in heterogeneous multi-relational networks
There has been a surge of interest in community detection in homogeneous
single-relational networks which contain only one type of nodes and edges.
However, many real-world systems are naturally described as heterogeneous
multi-relational networks which contain multiple types of nodes and edges. In
this paper, we propose a new method for detecting communities in such networks.
Our method is based on optimizing the composite modularity, which is a new
modularity proposed for evaluating partitions of a heterogeneous
multi-relational network into communities. Our method is parameter-free,
scalable, and suitable for various networks with general structure. We
demonstrate that it outperforms the state-of-the-art techniques in detecting
pre-planted communities in synthetic networks. Applied to a real-world Digg
network, it successfully detects meaningful communities.Comment: 27 pages, 10 figure
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
Eigenvector localization as a tool to study small communities in online social networks
We present and discuss a mathematical procedure for identification of small
"communities" or segments within large bipartite networks. The procedure is
based on spectral analysis of the matrix encoding network structure. The
principal tool here is localization of eigenvectors of the matrix, by means of
which the relevant network segments become visible. We exemplified our approach
by analyzing the data related to product reviewing on Amazon.com. We found
several segments, a kind of hybrid communities of densely interlinked reviewers
and products, which we were able to meaningfully interpret in terms of the type
and thematic categorization of reviewed items. The method provides a
complementary approach to other ways of community detection, typically aiming
at identification of large network modules
Exploring Communities in Large Profiled Graphs
Given a graph and a vertex , the community search (CS) problem
aims to efficiently find a subgraph of whose vertices are closely related
to . Communities are prevalent in social and biological networks, and can be
used in product advertisement and social event recommendation. In this paper,
we study profiled community search (PCS), where CS is performed on a profiled
graph. This is a graph in which each vertex has labels arranged in a
hierarchical manner. Extensive experiments show that PCS can identify
communities with themes that are common to their vertices, and is more
effective than existing CS approaches. As a naive solution for PCS is highly
expensive, we have also developed a tree index, which facilitate efficient and
online solutions for PCS
Detection and localization of change points in temporal networks with the aid of stochastic block models
A framework based on generalized hierarchical random graphs (GHRGs) for the
detection of change points in the structure of temporal networks has recently
been developed by Peel and Clauset [1]. We build on this methodology and extend
it to also include the versatile stochastic block models (SBMs) as a parametric
family for reconstructing the empirical networks. We use five different
techniques for change point detection on prototypical temporal networks,
including empirical and synthetic ones. We find that none of the considered
methods can consistently outperform the others when it comes to detecting and
locating the expected change points in empirical temporal networks. With
respect to the precision and the recall of the results of the change points, we
find that the method based on a degree-corrected SBM has better recall
properties than other dedicated methods, especially for sparse networks and
smaller sliding time window widths.Comment: This is an author-created, un-copyedited version of an article
accepted for publication/published in Journal of Statistical Mechanics:
Theory and Experiment. IOP Publishing Ltd is not responsible for any errors
or omissions in this version of the manuscript or any version derived from
it. The Version of Record is available online at
http://dx.doi.org/10.1088/1742-5468/2016/11/11330
Measuring the effect of node aggregation on community detection
Many times the nodes of a complex network, whether deliberately or not, are
aggregated for technical, ethical, legal limitations or privacy reasons. A
common example is the geographic position: one may uncover communities in a
network of places, or of individuals identified with their typical geographical
position, and then aggregate these places into larger entities, such as
municipalities, thus obtaining another network. The communities found in the
networks obtained at various levels of aggregation may exhibit various degrees
of similarity, from full alignment to perfect independence. This is akin to the
problem of ecological and atomic fallacies in statistics, or to the Modified
Areal Unit Problem in geography. We identify the class of community detection
algorithms most suitable to cope with node aggregation, and develop an index
for aggregability, capturing to which extent the aggregation preserves the
community structure. We illustrate its relevance on real-world examples (mobile
phone and Twitter reply-to networks). Our main message is that any
node-partitioning analysis performed on aggregated networks should be
interpreted with caution, as the outcome may be strongly influenced by the
level of the aggregation.Comment: 12 pages, 5 figure
Complementary network-based approaches for exploring genetic structure and functional connectivity in two vulnerable, endemic ground squirrels
The persistence of small populations is influenced by genetic structure and functional connectivity. We used two network-based approaches to understand the persistence of the northern Idaho ground squirrel (Urocitellus brunneus) and the southern Idaho ground squirrel (U. endemicus), two congeners of conservation concern. These graph theoretic approaches are conventionally applied to social or transportation networks, but here are used to study population persistence and connectivity. Population graph analyses revealed that local extinction rapidly reduced connectivity for the southern species, while connectivity for the northern species could be maintained following local extinction. Results from gravity models complemented those of population graph analyses, and indicated that potential vegetation productivity and topography drove connectivity in the northern species. For the southern species, development (roads) and small-scale topography reduced connectivity, while greater potential vegetation productivity increased connectivity. Taken together, the results of the two network-based methods (population graph analyses and gravity models) suggest the need for increased conservation action for the southern species, and that management efforts have been effective at maintaining habitat quality throughout the current range of the northern species. To prevent further declines, we encourage the continuation of management efforts for the northern species, whereas conservation of the southern species requires active management and additional measures to curtail habitat fragmentation. Our combination of population graph analyses and gravity models can inform conservation strategies of other species exhibiting patchy distributions
Multilayer Networks
In most natural and engineered systems, a set of entities interact with each
other in complicated patterns that can encompass multiple types of
relationships, change in time, and include other types of complications. Such
systems include multiple subsystems and layers of connectivity, and it is
important to take such "multilayer" features into account to try to improve our
understanding of complex systems. Consequently, it is necessary to generalize
"traditional" network theory by developing (and validating) a framework and
associated tools to study multilayer systems in a comprehensive fashion. The
origins of such efforts date back several decades and arose in multiple
disciplines, and now the study of multilayer networks has become one of the
most important directions in network science. In this paper, we discuss the
history of multilayer networks (and related concepts) and review the exploding
body of work on such networks. To unify the disparate terminology in the large
body of recent work, we discuss a general framework for multilayer networks,
construct a dictionary of terminology to relate the numerous existing concepts
to each other, and provide a thorough discussion that compares, contrasts, and
translates between related notions such as multilayer networks, multiplex
networks, interdependent networks, networks of networks, and many others. We
also survey and discuss existing data sets that can be represented as
multilayer networks. We review attempts to generalize single-layer-network
diagnostics to multilayer networks. We also discuss the rapidly expanding
research on multilayer-network models and notions like community structure,
connected components, tensor decompositions, and various types of dynamical
processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure
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