17,351 research outputs found
Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership
Detecting community structure in social networks is a fundamental problem
empowering us to identify groups of actors with similar interests. There have
been extensive works focusing on finding communities in static networks,
however, in reality, due to dynamic nature of social networks, they are
evolving continuously. Ignoring the dynamic aspect of social networks, neither
allows us to capture evolutionary behavior of the network nor to predict the
future status of individuals. Aside from being dynamic, another significant
characteristic of real-world social networks is the presence of leaders, i.e.
nodes with high degree centrality having a high attraction to absorb other
members and hence to form a local community. In this paper, we devised an
efficient method to incrementally detect communities in highly dynamic social
networks using the intuitive idea of importance and persistence of community
leaders over time. Our proposed method is able to find new communities based on
the previous structure of the network without recomputing them from scratch.
This unique feature, enables us to efficiently detect and track communities
over time rapidly. Experimental results on the synthetic and real-world social
networks demonstrate that our method is both effective and efficient in
discovering communities in dynamic social networks
Graphs in machine learning: an introduction
Graphs are commonly used to characterise interactions between objects of
interest. Because they are based on a straightforward formalism, they are used
in many scientific fields from computer science to historical sciences. In this
paper, we give an introduction to some methods relying on graphs for learning.
This includes both unsupervised and supervised methods. Unsupervised learning
algorithms usually aim at visualising graphs in latent spaces and/or clustering
the nodes. Both focus on extracting knowledge from graph topologies. While most
existing techniques are only applicable to static graphs, where edges do not
evolve through time, recent developments have shown that they could be extended
to deal with evolving networks. In a supervised context, one generally aims at
inferring labels or numerical values attached to nodes using both the graph
and, when they are available, node characteristics. Balancing the two sources
of information can be challenging, especially as they can disagree locally or
globally. In both contexts, supervised and un-supervised, data can be
relational (augmented with one or several global graphs) as described above, or
graph valued. In this latter case, each object of interest is given as a full
graph (possibly completed by other characteristics). In this context, natural
tasks include graph clustering (as in producing clusters of graphs rather than
clusters of nodes in a single graph), graph classification, etc. 1 Real
networks One of the first practical studies on graphs can be dated back to the
original work of Moreno [51] in the 30s. Since then, there has been a growing
interest in graph analysis associated with strong developments in the modelling
and the processing of these data. Graphs are now used in many scientific
fields. In Biology [54, 2, 7], for instance, metabolic networks can describe
pathways of biochemical reactions [41], while in social sciences networks are
used to represent relation ties between actors [66, 56, 36, 34]. Other examples
include powergrids [71] and the web [75]. Recently, networks have also been
considered in other areas such as geography [22] and history [59, 39]. In
machine learning, networks are seen as powerful tools to model problems in
order to extract information from data and for prediction purposes. This is the
object of this paper. For more complete surveys, we refer to [28, 62, 49, 45].
In this section, we introduce notations and highlight properties shared by most
real networks. In Section 2, we then consider methods aiming at extracting
information from a unique network. We will particularly focus on clustering
methods where the goal is to find clusters of vertices. Finally, in Section 3,
techniques that take a series of networks into account, where each network i
On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs
Modern networks are of huge sizes as well as high dynamics, which challenges
the efficiency of community detection algorithms. In this paper, we study the
problem of overlapping community detection on distributed and dynamic graphs.
Given a distributed, undirected and unweighted graph, the goal is to detect
overlapping communities incrementally as the graph is dynamically changing. We
propose an efficient algorithm, called \textit{randomized Speaker-Listener
Label Propagation Algorithm} (rSLPA), based on the \textit{Speaker-Listener
Label Propagation Algorithm} (SLPA) by relaxing the probability distribution of
label propagation. Besides detecting high-quality communities, rSLPA can
incrementally update the detected communities after a batch of edge insertion
and deletion operations. To the best of our knowledge, rSLPA is the first
algorithm that can incrementally capture the same communities as those obtained
by applying the detection algorithm from the scratch on the updated graph.
Extensive experiments are conducted on both synthetic and real-world datasets,
and the results show that our algorithm can achieve high accuracy and
efficiency at the same time.Comment: A short version of this paper will be published as ICDE'2018 poste
Graphical Analysis of Social Group Dynamics
Identifying communities in social networks becomes an increasingly important
research problem. Several methods for identifying such groups have been
developed, however, qualitative analysis (taking into account the scale of the
problem) still poses serious problems. This paper describes a tool for
facilitating such an analysis, allowing to visualize the dynamics and
supporting localization of different events (such as creation or merging of
groups). In the final part of the paper, the experimental results performed
using the benchmark data (Enron emails) provide an insight into usefulness of
the proposed tool.Comment: Fourth International Conference on Computational Aspects of Social
Networks, CASoN 2012, Sao Carlos, Brazil, November 21-23, 2012, pp. 41-46;
IEEE Computer Society, 201
Uncovering the Wider Structure of Extreme Right Communities Spanning Popular Online Networks
Recent years have seen increased interest in the online presence of extreme
right groups. Although originally composed of dedicated websites, the online
extreme right milieu now spans multiple networks, including popular social
media platforms such as Twitter, Facebook and YouTube. Ideally therefore, any
contemporary analysis of online extreme right activity requires the
consideration of multiple data sources, rather than being restricted to a
single platform. We investigate the potential for Twitter to act as a gateway
to communities within the wider online network of the extreme right, given its
facility for the dissemination of content. A strategy for representing
heterogeneous network data with a single homogeneous network for the purpose of
community detection is presented, where these inherently dynamic communities
are tracked over time. We use this strategy to discover and analyze persistent
English and German language extreme right communities.Comment: 10 pages, 11 figures. Due to use of "sigchi" template, minor changes
were made to ensure 10 page limit was not exceeded. Minor clarifications in
Introduction, Data and Methodology section
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