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Optimizing Differentially-Maintained Recursive Queries on Dynamic Graphs

Summary: Mitigates memory blowup in differential maintenance for recursive graph queries by dropping operator differences and recomputing. Uses deterministic and probabilistic structures to track dropped diffs, enabling scalable DC-based query maintenance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12799
Venue
VLDB
Year
2022
Pagerank
4.2400894e-05
Overall Rank
9,954 | 30.76%
DOI
10.14778/3551793.3551862

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,404 Dynamic Pruning for Recursive Joins 2025 SIGMOD 4.1945683e-05
10,745 Robust Recursive Query Parallelism in Graph Database Management Systems 2025 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
522 Differential dataflow 2013 CIDR 0.00021099241
536 The LDBC Social Network Benchmark: Interactive Workload 2015 SIGMOD 0.00020722862
1,756 Graphflow: An Active Graph Database 2017 SIGMOD 0.00010664542
2,826 Regular Path Query Evaluation on Streaming Graphs 2020 SIGMOD 8.056119e-05
5,570 iTurboGraph: Scaling and Automating Incremental Graph Analytics 2021 SIGMOD 5.4284968e-05
8,795 Graphsurge: Graph Analytics on View Collections Using Differential Computation 2021 SIGMOD 4.4499973e-05
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