AI

Efficient Densest Subgraph Computation in Evolving Graphs

Abstract

Densest subgraph computation has emerged as an important primitive in a wide range of data analysis tasks such as community and event detection. Social media such as Facebook and Twitter are highly dynamic with new friendship links and tweets being generated incessantly, calling for efficient algorithms that can handle very large and highly dynamic input data. While either scalable or dynamic algorithms for finding densest subgraphs have been proposed, a viable and satisfactory solution for addressing both the dynamic aspect of the input data and its large size is still missing. We study the densest subgraph problem in the the dynamic graph model, for which we present the first scalable algorithm with provable guarantees. In our model, edges are added adversarially while they are removed uniformly at random from the current graph. We show that at any point in time we are able to maintain a 2(1+ε)-approximation of a current densest subgraph, while requiring O(polylog(n+r)) amortized cost per update (with high probability), where r is the total number of update operations executed and n is the maximum number of nodes in the graph. In contrast, a naive algorithm that recomputes a dense subgraph every time the graph changes requires Omega(m) work per update, where m is the number of edges in the current graph. Our theoretical analysis is complemented with an extensive experimental evaluation on large real-world graphs showing that (approximate) densest subgraphs can be maintained efficiently within hundred of microseconds per update.