Reducing Re-Indexing for Top-k Personalized PageRank Computation on Dynamic Graphs

Top-k Personalized PageRank (PPR) is a graph analysis method used to determine the $k$ most important nodes with respect to a source node. To realize fast Top-k PPR computation, indexing for each node is effective. When we apply the index-based Top-k PPR methods to dynamic graphs, the index becomes stale with edge updates, and index correction is required. Although the existing methods perform index correction for every update to guarantee Top-k PPR accuracy, they involve heavy re-indexing computation or significant memory overhead. This paper proposes a method that achieves comparable accuracy to guaranteed methods while significantly reducing re-indexing by focusing on the fact that index references are concentrated on the nodes whose index is unlikely to change due to edge updates. In particular, our method omits re-indexing as long as we achieve comparable accuracy. Furthermore, our method involves the minimum memory overhead among the existing index-based methods. The space complexity of the index is $\Theta (n + m)$, where $n$ and $m$ are the number of nodes and edges of the graph, respectively. The evaluation results using real-world datasets show that our method achieves more than 0.999 Normalized Discounted Cumulative Gain until 20% of edges are updated from index generation.