Node Similarity With Q-Grams For Real-World Labeled Networks

Authors:
Roberto Grossi University of Pisa
Alessio Conte University of Pisa
Gaspare Ferraro University of Pisa
Andrea Marino University of Pisa
Kunihiko Sadakane University of Tokyo
Takeaki Uno National Institute of Informatics, Japan

Introduction:

This paper studies node similarity in labeled networks, using the label sequences found in paths of bounded length q leading to the nodes.

Abstract:

We study node similarity in labeled networks, using the label sequences found in paths of bounded length q leading to the nodes. (This recalls the q-grams employed in document resemblance, based on the Jaccard distance.) When applied to networks, the challenge is two-fold: the number of q-grams generated from labeled paths grows exponentially with q, and their frequency should be taken into account: this leads to a variation of the Jaccard index known as Bray-Curtis index for multisets. We describe nSimGram, a suite of fast algorithms for node similarity with q-grams, based on a novel blend of color coding, probabilistic counting, sketches, and string algorithms, where the universe of elements to sample is exponential. We provide experimental evidence that our measure is effective and our running times scale to deal with large real-world networks.

You may want to know: