27,163 research outputs found
Distance-generalized Core Decomposition
The -core of a graph is defined as the maximal subgraph in which every
vertex is connected to at least other vertices within that subgraph. In
this work we introduce a distance-based generalization of the notion of
-core, which we refer to as the -core, i.e., the maximal subgraph in
which every vertex has at least other vertices at distance within
that subgraph. We study the properties of the -core showing that it
preserves many of the nice features of the classic core decomposition (e.g.,
its connection with the notion of distance-generalized chromatic number) and it
preserves its usefulness to speed-up or approximate distance-generalized
notions of dense structures, such as -club.
Computing the distance-generalized core decomposition over large networks is
intrinsically complex. However, by exploiting clever upper and lower bounds we
can partition the computation in a set of totally independent subcomputations,
opening the door to top-down exploration and to multithreading, and thus
achieving an efficient algorithm
Considerations about multistep community detection
The problem and implications of community detection in networks have raised a
huge attention, for its important applications in both natural and social
sciences. A number of algorithms has been developed to solve this problem,
addressing either speed optimization or the quality of the partitions
calculated. In this paper we propose a multi-step procedure bridging the
fastest, but less accurate algorithms (coarse clustering), with the slowest,
most effective ones (refinement). By adopting heuristic ranking of the nodes,
and classifying a fraction of them as `critical', a refinement step can be
restricted to this subset of the network, thus saving computational time.
Preliminary numerical results are discussed, showing improvement of the final
partition.Comment: 12 page
Supporting social innovation through visualisations of community interactions
Online communities that form through the introduction of sociotechnical platforms require significant effort to cultivate and sustain. Providing open, transparent information on community behaviour can motivate participation from community members themselves, while also providing platform administrators with detailed interaction dynamics. However, challenges arise in both understanding what information is conducive to engagement and sustainability, and then how best to represent this information to platform stakeholders. Towards a better understanding of these challenges, we present the design, implementation, and evaluation of a set of simple visualisations integrated into a Collective Awareness Platform for Social Innovation platform titled commonfare.net. We discuss the promise and challenge of bringing social innovation into the digital age, in terms of supporting sustained platform use and collective action, and how the introduction of community visualisations has been directed towards achieving this goal
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