Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion

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.

Finding MNEMON: Reviving Memories of Node Embeddings

“…Note that the attackers can estimate the graph size (i.e., the number of edges) which equals to k × n/2. We use the state-of-the-art spikyball sampling [58] to estimate the average node degree for our evaluation. It generalizes several exploration-based sampling schemes (e.g., Snowball sampling, Forest Fire sampling, graph-expander sampling etc.…”

“…It generalizes several exploration-based sampling schemes (e.g., Snowball sampling, Forest Fire sampling, graph-expander sampling etc. ), and can be applied to any large graphs due to its flexibility [58]. Specifically, for citation/co-occurrence graphs (e.g., Cora, Citeseer, and Actor), we use the publicly available citation graphs -Pubmed and DBLP -to estimate the average node degree.…”