We propose a new exact method for shortest-path distance queries on large-scale networks. Our method precomputes distance labels for vertices by performing a breadth-first search from every vertex. Seemingly too obvious and too inefficient at first glance, the key ingredient introduced here is pruning during breadth-first searches. While we can still answer the correct distance for any pair of vertices from the labels, it surprisingly reduces the search space and sizes of labels. Moreover, we show that we can perform 32 or 64 breadth-first searches simultaneously exploiting bitwise operations. We experimentally demonstrate that the combination of these two techniques is efficient and robust on various kinds of large-scale real-world networks. In particular, our method can handle social networks and web graphs with hundreds of millions of edges, which are two orders of magnitude larger than the limits of previous exact methods, with comparable query time to those of previous methods.

A recent trend in parameterized algorithms is the application of polytope tools (specifically, LPbranching) to FPT algorithms (e.g., Cygan et al., 2011;. Though the list of work in this direction is short, the results are already interesting, yielding significant speedups for a range of important problems. However, the existing approaches require the underlying polytope to have very restrictive properties, including half-integrality and Nemhauser-Trotter-style persistence properties. To date, these properties are essentially known to hold only for two classes of polytopes, covering the cases of Vertex Cover (Nemhauser and Trotter, 1975) and Node Multiway Cut (Garg et al., 1994).Taking a slightly different approach, we view half-integrality as a discrete relaxation of a problem, e.g., a relaxation of the search space from {0, 1}V to {0, 1 /2, 1} V such that the new problem admits a polynomial-time exact solution. Using tools from CSP (in particular Thapper andŽivný, 2012) to study the existence of such relaxations, we are able to provide a much broader class of half-integral polytopes with the required properties.Our results unify and significantly extend the previously known cases. In addition to the new insight into problems with half-integral relaxations, our results yield a range of new and improved FPT algorithms, including an O * (|Σ| 2k )-time algorithm for node-deletion Unique Label Cover with label set

We propose the first real-time fully-dynamic index data structure designed for influence analysis on evolving networks. With this aim, we carefully redesign the data structure of the state-of-the-art sketching method introduced by Borgs et al., and construct corresponding update algorithms. Using this index, we present algorithms for two kinds of queries, influence estimation and influence maximization, which are strongly motivated by practical applications, such as viral marketing. We provide a thorough theoretical analysis, which guarantees the non-degeneracy of the solution accuracy after an arbitrary number of updates. Furthermore, we introduce a reachability-tree-based technique and a skipping method, which greatly reduce the time consumption required for edge/vertex deletions and vertex additions, respectively, and counter-based random number generators, which improve the space efficiency. Experimental evaluations using real dynamic networks with tens of millions of edges demonstrate the efficiency, scalability, and accuracy of our proposed indexing scheme. Specifically, it can reflect a graph modification within a time of several orders of magnitude smaller than that required to reconstruct an index from scratch, estimate the influence spread of a vertex set accurately within a millisecond, and select highly influential vertices at least ten times faster than state-of-the-art static algorithms.

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