11,504 research outputs found
Deciphering Network Community Structure by Surprise
The analysis of complex networks permeates all sciences, from biology to
sociology. A fundamental, unsolved problem is how to characterize the community
structure of a network. Here, using both standard and novel benchmarks, we show
that maximization of a simple global parameter, which we call Surprise (S),
leads to a very efficient characterization of the community structure of
complex synthetic networks. Particularly, S qualitatively outperforms the most
commonly used criterion to define communities, Newman and Girvan's modularity
(Q). Applying S maximization to real networks often provides natural,
well-supported partitions, but also sometimes counterintuitive solutions that
expose the limitations of our previous knowledge. These results indicate that
it is possible to define an effective global criterion for community structure
and open new routes for the understanding of complex networks.Comment: 7 pages, 5 figure
Generalized Markov stability of network communities
We address the problem of community detection in networks by introducing a
general definition of Markov stability, based on the difference between the
probability fluxes of a Markov chain on the network at different time scales.
The specific implementation of the quality function and the resulting optimal
community structure thus become dependent both on the type of Markov process
and on the specific Markov times considered. For instance, if we use a natural
Markov chain dynamics and discount its stationary distribution -- that is, we
take as reference process the dynamics at infinite time -- we obtain the
standard formulation of the Markov stability. Notably, the possibility to use
finite-time transition probabilities to define the reference process naturally
allows detecting communities at different resolutions, without the need to
consider a continuous-time Markov chain in the small time limit. The main
advantage of our general formulation of Markov stability based on dynamical
flows is that we work with lumped Markov chains on network partitions, having
the same stationary distribution of the original process. In this way the form
of the quality function becomes invariant under partitioning, leading to a
self-consistent definition of community structures at different aggregation
scales
Link-Prediction Enhanced Consensus Clustering for Complex Networks
Many real networks that are inferred or collected from data are incomplete
due to missing edges. Missing edges can be inherent to the dataset (Facebook
friend links will never be complete) or the result of sampling (one may only
have access to a portion of the data). The consequence is that downstream
analyses that consume the network will often yield less accurate results than
if the edges were complete. Community detection algorithms, in particular,
often suffer when critical intra-community edges are missing. We propose a
novel consensus clustering algorithm to enhance community detection on
incomplete networks. Our framework utilizes existing community detection
algorithms that process networks imputed by our link prediction based
algorithm. The framework then merges their multiple outputs into a final
consensus output. On average our method boosts performance of existing
algorithms by 7% on artificial data and 17% on ego networks collected from
Facebook
Fast community structure local uncovering by independent vertex-centred process
This paper addresses the task of community detection and proposes a local
approach based on a distributed list building, where each vertex broadcasts
basic information that only depends on its degree and that of its neighbours. A
decentralised external process then unveils the community structure. The
relevance of the proposed method is experimentally shown on both artificial and
real data.Comment: 2015 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, Aug 2015, Paris, France. Proceedings of the 2015
IEEE/ACM International Conference on Advances in Social Networks Analysis and
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