Incremental Proximity Graph Maintenance for Online Nearest Neighbor Search
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Solution Overview
Problem
Current graph-based Approximate Nearest Neighbor (ANN) search methods lack an efficient mechanism for online vertex deletion, leading to performance drops due to broken connectivity and increased computational costs in dynamic data environments, such as real-world recommendation systems.
Innovation Solution
The implementation of an incremental proximity graph maintenance (IPGM) process that supports both online vertex deletion and insertion by updating connections using local or global reconnection methods, maintaining the proximity graph's properties without reconstructing the entire graph, thereby preserving search efficiency and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire proximity graph is reconstructed after vertex deletion, then the connectivity and search precision are restored, but the computational cost and time consumption increase significantly
Solution Approach 1:
The patent segments the graph update process into localized operations around the deleted vertex rather than global reconstruction. Specifically, it identifies and updates only the affected subgraph components (neighbors and their neighbors) while leaving the rest of the graph intact, thereby reducing the time cost from O(n) to O(k) where k is the number of affected vertices
Solution Approach 2:
The patent performs preliminary actions by pre-computing and storing neighbor relationships in the graph structure. When a vertex is deleted, the system has already organized the data so that only local reconnection is needed - the neighbors of the deleted vertex can be quickly identified and reconnected without scanning the entire graph, thus restoring precision efficiently
2Productivity
If the proximity graph is updated incrementally with local reconnection only, then the computational cost is reduced, but the search precision and connectivity may deteriorate
Solution Approach 1:
The patent applies local quality by making the reconnection operation adaptive to the local structure around the deleted vertex. The algorithm performs thorough reconnection in the affected local region (where precision matters most) while avoiding unnecessary operations in unaffected regions, thus maintaining high precision where needed while preserving overall productivity
Solution Approach 2:
The patent ensures continuity of useful action by maintaining the graph's connectivity properties through incremental updates. The local reconnection process continuously preserves the essential proximity relationships and path connectivity, ensuring that search operations remain effective without requiring periodic full reconstructions
3Adaptability or versatility
If vertex deletion is implemented in online fashion, then the system can handle dynamic data distributions, but the graph connectivity breaks and performance drops
Solution Approach 1:
The patent implements dynamic graph maintenance that adapts to online vertex deletions. The system dynamically identifies affected regions and applies reconnection operations only where needed, allowing the graph to adapt to changing data distributions while preserving query performance. The dynamic nature of the update process maintains adaptability without sacrificing speed
Data Source
AI summary
Incremental proximity graph maintenance (IPGM) systems and methods for online ANN search support both online vertex deletion and insertion of vertices on proximity graphs. In various embodiments, updating a proximity graph comprises receiving a workload that represents a set of vertices in the proximity graph, each vertex being associated with a type of operation such as a query, insertion, or deletion. For a query or an insertion, a search may be executed on the graph to obtain a set of top-K vertices for each vertex. In the case of a deletion, a vertex may be deleted from the proximity graph, and a local or global reconnection update method may be used to reconstruct at least a portion of the proximity graph.