1,008 research outputs found
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
Algorithms and Software for the Analysis of Large Complex Networks
The work presented intersects three main areas, namely graph algorithmics, network science and applied software engineering. Each computational method discussed relates to one of the main tasks of data analysis: to extract structural features from network data, such as methods for community detection; or to transform network data, such as methods to sparsify a network and reduce its size while keeping essential properties; or to realistically model networks through generative models
Book of Abstracts of the Sixth SIAM Workshop on Combinatorial Scientific Computing
Book of Abstracts of CSC14 edited by Bora UçarInternational audienceThe Sixth SIAM Workshop on Combinatorial Scientific Computing, CSC14, was organized at the Ecole Normale Supérieure de Lyon, France on 21st to 23rd July, 2014. This two and a half day event marked the sixth in a series that started ten years ago in San Francisco, USA. The CSC14 Workshop's focus was on combinatorial mathematics and algorithms in high performance computing, broadly interpreted. The workshop featured three invited talks, 27 contributed talks and eight poster presentations. All three invited talks were focused on two interesting fields of research specifically: randomized algorithms for numerical linear algebra and network analysis. The contributed talks and the posters targeted modeling, analysis, bisection, clustering, and partitioning of graphs, applied in the context of networks, sparse matrix factorizations, iterative solvers, fast multi-pole methods, automatic differentiation, high-performance computing, and linear programming. The workshop was held at the premises of the LIP laboratory of ENS Lyon and was generously supported by the LABEX MILYON (ANR-10-LABX-0070, Université de Lyon, within the program ''Investissements d'Avenir'' ANR-11-IDEX-0007 operated by the French National Research Agency), and by SIAM
Growing networks of overlapping communities with internal structure
We introduce an intuitive model that describes both the emergence of
community structure and the evolution of the internal structure of communities
in growing social networks. The model comprises two complementary mechanisms:
One mechanism accounts for the evolution of the internal link structure of a
single community, and the second mechanism coordinates the growth of multiple
overlapping communities. The first mechanism is based on the assumption that
each node establishes links with its neighbors and introduces new nodes to the
community at different rates. We demonstrate that this simple mechanism gives
rise to an effective maximal degree within communities. This observation is
related to the anthropological theory known as Dunbar's number, i.e., the
empirical observation of a maximal number of ties which an average individual
can sustain within its social groups. The second mechanism is based on a
recently proposed generalization of preferential attachment to community
structure, appropriately called structural preferential attachment (SPA). The
combination of these two mechanisms into a single model (SPA+) allows us to
reproduce a number of the global statistics of real networks: The distribution
of community sizes, of node memberships and of degrees. The SPA+ model also
predicts (a) three qualitative regimes for the degree distribution within
overlapping communities and (b) strong correlations between the number of
communities to which a node belongs and its number of connections within each
community. We present empirical evidence that support our findings in real
complex networks.Comment: 14 pages, 8 figures, 2 table
Simplifying large-scale communication networks with weights and cycles
PhDA communication network is a complex network designed to transfer information from a
source to a destination. One of the most important property in a communication network is
the existence of alternative routes between a source and destination node. The robustness
and resilience of a network are related to its path diversity (alternative routes). Describing
all the components and interactions of a large communication network is not feasible. In
this thesis we develop a new method, the deforestation algorithm, to simplify very large
networks, and we called the simplified network the skeleton network. The method is general.
It conserves the number of alternative paths between all the sources and destinations when
doing the simplification and also it takes into consideration the properties of the nodes, and
the links (capacity and direction).
When simplifying very large networks, the skeleton networks can also be large, so it is
desirable to split the skeleton network into different communities. In the thesis we introduce
a community-detection method which works fast and efficient for the skeleton networks.
Other property that can be easily extracted from the skeleton network is the cycle basis,
which can suffice in describing the cycle structure of complex network.
We have tested our algorithms on the Autonomous System (AS)l evel and Internet Protocol
address (IPA)le vel of the Internet. And we also show that deforestation algorithm can be
extended to take into consideration of traffic directions and traffic demand matrix when
simplifying medium-scale networks.
Commonly, the structure of large complex networks is characterised using statistical measures.
These measures can give a good description of the network connectivity but they do
not provide a practical way to explore the interaction between the dynamical process and
network connectivity. The methods presented in this thesis are a first step to address this
practical problem
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