BatchHL: Answering Distance Queries on Batch-Dynamic Networks at Scale
Summary: BatchHL is a batch-dynamic framework for distance queries on evolving graphs, pairing a compact offline distance labelling with online search. It updates labellings under batch changes, with correctness, minimality, and complexity guarantees, plus validation on 14 real networks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Muhammad Farhan
- 2. Qing Wang
- 3. Henning Koehler
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,484 | Enabling Window-Based Monotonic Graph Analytics with Reusable Transitional Results for Pattern-Consistent Queries | 2024 | VLDB | 4.3341665e-05 |
| 10,467 | Accelerating Skyline Path Enumeration with a Core Attribute Index on Multi-attribute Graphs | 2025 | SIGMOD | 4.1945683e-05 |
| 10,584 | Efficient Maintenance of 2-Hop Labeling Index on Dynamic Small-World Graphs | 2025 | VLDB | 4.1945683e-05 |
| 10,865 | Approximate Anchored Densest Subgraph Search on Large Static and Dynamic Graphs | 2025 | VLDB | 4.1945683e-05 |
| 10,874 | A CPU-GPU Hybrid Labelling Algorithm for Massive Shortest Distance Queries on Road Networks | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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