Skip hub nodes and include block nodes in sampling#60
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This PR introduces two major improvements to the sampling algorithm:
Ability to skip hub nodes: Hub nodes (e.g., exchange or mixer nodes) are characterized by a highly significant in/out-degree. Because two nodes connected through a hub are not necessarily related, including a hub node and its subsequent connections can artificially inflate and mislead the sampled neighborhood. To address this, we added an option to define a traversal stopping criterion: the algorithm will no longer expand beyond a node if its in/out-degree exceeds a specified threshold.
Automatic block node inclusion: In domains like finance,
Blocknodes provide crucial macroeconomic context for a transaction. Pairing a transaction (Tx) node with its respectiveBlocknode yields richer features for downstream learning tasks. This PR adds a configuration option ensuring that everyTxnode sampled into a neighborhood automatically brings its correspondingBlocknode with it.Additionally, this PR renames the sampling algorithm from
Forest FiretoPanorama. Our implementation has evolved far beyond the originalForest Fireconcept, and updating the name prevents confusion moving forward.