4,929 research outputs found
Fast filtering and animation of large dynamic networks
Detecting and visualizing what are the most relevant changes in an evolving
network is an open challenge in several domains. We present a fast algorithm
that filters subsets of the strongest nodes and edges representing an evolving
weighted graph and visualize it by either creating a movie, or by streaming it
to an interactive network visualization tool. The algorithm is an approximation
of exponential sliding time-window that scales linearly with the number of
interactions. We compare the algorithm against rectangular and exponential
sliding time-window methods. Our network filtering algorithm: i) captures
persistent trends in the structure of dynamic weighted networks, ii) smoothens
transitions between the snapshots of dynamic network, and iii) uses limited
memory and processor time. The algorithm is publicly available as open-source
software.Comment: 6 figures, 2 table
Tiles: an online algorithm for community discovery in dynamic social networks
Community discovery has emerged during the last decade as one of the most challenging problems in social network analysis. Many algorithms have been proposed to find communities on static networks, i.e. networks which do not change in time. However, social networks are dynamic realities (e.g. call graphs, online social networks): in such scenarios static community discovery fails to identify a partition of the graph that is semantically consistent with the temporal information expressed by the data. In this work we propose Tiles, an algorithm that extracts overlapping communities and tracks their evolution in time following an online iterative procedure. Our algorithm operates following a domino effect strategy, dynamically recomputing nodes community memberships whenever a new interaction takes place. We compare Tiles with state-of-the-art community detection algorithms on both synthetic and real world networks having annotated community structure: our experiments show that the proposed approach is able to guarantee lower execution times and better correspondence with the ground truth communities than its competitors. Moreover, we illustrate the specifics of the proposed approach by discussing the properties of identified communities it is able to identify
A Fast and Efficient Incremental Approach toward Dynamic Community Detection
Community detection is a discovery tool used by network scientists to analyze
the structure of real-world networks. It seeks to identify natural divisions
that may exist in the input networks that partition the vertices into coherent
modules (or communities). While this problem space is rich with efficient
algorithms and software, most of this literature caters to the static use-case
where the underlying network does not change. However, many emerging real-world
use-cases give rise to a need to incorporate dynamic graphs as inputs.
In this paper, we present a fast and efficient incremental approach toward
dynamic community detection. The key contribution is a generic technique called
, which examines the most recent batch of changes made to an
input graph and selects a subset of vertices to reevaluate for potential
community (re)assignment. This technique can be incorporated into any of the
community detection methods that use modularity as its objective function for
clustering. For demonstration purposes, we incorporated the technique into two
well-known community detection tools. Our experiments demonstrate that our new
incremental approach is able to generate performance speedups without
compromising on the output quality (despite its heuristic nature). For
instance, on a real-world network with 63M temporal edges (over 12 time steps),
our approach was able to complete in 1056 seconds, yielding a 3x speedup over a
baseline implementation. In addition to demonstrating the performance benefits,
we also show how to use our approach to delineate appropriate intervals of
temporal resolutions at which to analyze an input network
Community Detection in Dynamic Networks via Adaptive Label Propagation
An adaptive label propagation algorithm (ALPA) is proposed to detect and
monitor communities in dynamic networks. Unlike the traditional methods by
re-computing the whole community decomposition after each modification of the
network, ALPA takes into account the information of historical communities and
updates its solution according to the network modifications via a local label
propagation process, which generally affects only a small portion of the
network. This makes it respond to network changes at low computational cost.
The effectiveness of ALPA has been tested on both synthetic and real-world
networks, which shows that it can successfully identify and track dynamic
communities. Moreover, ALPA could detect communities with high quality and
accuracy compared to other methods. Therefore, being low-complexity and
parameter-free, ALPA is a scalable and promising solution for some real-world
applications of community detection in dynamic networks.Comment: 16 pages, 11 figure
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