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EAGr: Supporting Continuous Ego-centric Aggregate Queries over Large Dynamic Graphs

Summary: Proposes EAGr, an in-memory system for ego-centric aggregates on dynamic graphs. Aggregation overlay graph enables sharing partial results and precomputation for low-latency updates and scalable single-machine deployment (up to 320M nodes/edges, 500k+ ops/s). (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4914
Venue
SIGMOD
Year
2014
Pagerank
4.8462038e-05
Overall Rank
7,062 | 50.88%
DOI
-

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

Rank Cited Paper Year Venue Pagerank
140 The MADlib Analytics Library or MAD Skills, the SQL 2012 VLDB 0.00042270404
181 Mining Frequent Patterns without Candidate Generation 2000 SIGMOD 0.00036992674
1,139 Feeding Frenzy: Selectively Materializing Users' Event Feeds 2010 SIGMOD 0.00013725337
1,554 Resource Sharing in Continuous Sliding-Window Aggregates 2004 VLDB 0.00011400581
1,788 On-the-Fly Sharing for Streamed Aggregation 2006 SIGMOD 0.00010555742
3,232 Managing Large Dynamic Graphs Efficiently 2012 SIGMOD 7.336861e-05
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