The problem of point-to-point fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speed-up the computation. Most of the existing approaches make the simplifying assumption that travel-times of the network edges are constant. However, with real-world spatial networks the edge travel-times are time-dependent, where the arrival-time to an edge determines the actual travel-time on the edge. In this paper, we study the online computation of fastest path in time-dependent spatial networks and present a technique which speeds-up the path computation. We show that our fastest path computation based on a bidirectional time-dependent A * search significantly improves the computation time and storage complexity. With extensive experiments using real data-sets (including a variety of large spatial networks with real traffic data) we demonstrate the efficacy of our proposed techniques for online fastest path computation.

Abstract. Given a spatio-temporal network, a source, a destination, and a start-time interval, the All-start-time Lagrangian Shortest Paths (ALSP) problem determines a path set which includes the shortest path for every start time in the given interval. ALSP is important for critical societal applications related to air travel, road travel, and other spatiotemporal networks. However, ALSP is computationally challenging due to the non-stationary ranking of the candidate paths, meaning that a candidate path which is optimal for one start time may not be optimal for others. Determining a shortest path for each start-time leads to redundant computations across consecutive start times sharing a common solution. The proposed approach reduces this redundancy by determining the critical time points at which an optimal path may change. Theoretical analysis and experimental results show that this approach performs better than naive approaches particularly when there are few critical time points. 1

Given a dynamic social network, a discrete time interval, and a bound on the lifetime of a unit of information, the Dynamic INFormation Liaison (DINFL) problem aims to find a set of individuals that includes the most “information relaying ” (or central) person for each time in the given interval. This problem is important for several societal applications related to viral marketing, word-of-mouth marketing, finding key influences etc. However, DINFL poses both semantic and computational challenges. Semantic challenges requires us to model realistic pathways through which information can flow. This involves modeling semantics of information flow such as bounded lifetime of information (i.e. discussions on a topic last only finite amount of time). On the other hand, computational challenges arise due the inherent non-stationarity within a dynamic social network. Consequently, centrality of individuals in the network change with time. Related work in this area has been limited due to two reasons. First, existing computational methods to solve DINFL are likely to incur exorbitant computational costs as they would have to compute the centrality value of each time instant in the given time interval to ensure correctness. Second, they lack adequate models to capture the semantics of information flow, such lifetime of information. In contrast, this paper proposes a novel centrality metric called, Information lifetime aware (or InLife) betweenness centrality, which considers the semantic issues of information flow. A detailed case study shows how our metric compares with the traditional notion of

Abstract. We present the first approximate distance oracle for sparse directed networks with time-dependent arc-travel-times determined by continuous, piecewise linear, positive functions possessing the FIFO prop-erty. Our approach precomputes (1 + ε)−approximate distance sum-maries from selected landmark vertices to all other vertices in the network, and provides two sublinear-time query algorithms that deliver constant and (1+σ)−approximate shortest-travel-times, respectively, for arbitrary origin-destination pairs in the network. Our oracle is based only on the sparsity of the network, along with two quite natural assumptions about travel-time functions which allow the smooth transition towards asymmetric and time-dependent distance metrics. 1