TL;DR
This paper introduces KARPET, an anytime algorithm for top-k pattern retrieval in labeled graphs, providing efficient, incremental search with strong guarantees, suitable for large heterogeneous networks and user-driven exploration.
Contribution
It proposes the first practical anytime top-k search algorithm for acyclic patterns in labeled graphs, leveraging pruning and guided search for efficiency.
Findings
KARPET achieves millisecond response times on large networks.
The algorithm effectively exploits label constraints and acyclic structure.
Strong theoretical guarantees on time and space complexity.
Abstract
Many problems in areas as diverse as recommendation systems, social network analysis, semantic search, and distributed root cause analysis can be modeled as pattern search on labeled graphs (also called "heterogeneous information networks" or HINs). Given a large graph and a query pattern with node and edge label constraints, a fundamental challenge is to nd the top-k matches ac- cording to a ranking function over edge and node weights. For users, it is di cult to select value k . We therefore propose the novel notion of an any-k ranking algorithm: for a given time budget, re- turn as many of the top-ranked results as possible. Then, given additional time, produce the next lower-ranked results quickly as well. It can be stopped anytime, but may have to continues until all results are returned. This paper focuses on acyclic patterns over arbitrary labeled graphs. We are interested in…
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