Spikyball sampling: Exploring large networks via an inhomogeneous filtered diffusion

TL;DR

Spikyball sampling is a novel network exploration method that efficiently samples relevant parts of large, complex networks by combining filtered diffusion with random neighbor selection, capturing important structures while reducing noise.

Contribution

It introduces a new sampling approach that generalizes existing methods, enabling targeted exploration of large networks with improved focus on relevant node groups.

Findings

Effectively captures high-interaction node groups

Reduces noise by discarding weakly connected nodes

Extends and generalizes previous sampling methods

Abstract

Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatically sample user-defined relevant parts of a network. Starting from a few selected places in the network and a reduced set of expansion rules, the method adopts a filtered breadth-first search approach, that expands through edges and nodes matching these properties. Moreover, the expansion is performed over a random subset of neighbors at each step to mitigate further the overwhelming number of connections that may exist in large graphs. This carries the image of a "spiky" expansion. We show that this approach generalize previous exploration sampling methods, such as Snowball or Forest Fire and extend them. We demonstrate its ability to capture groups of nodes with high interactions while discarding weakly connected nodes that are often numerous in social networks and may hide important structures.