49,401 research outputs found
Fast Shortest Path Distance Estimation in Large Networks
We study the problem of preprocessing a large graph so that point-to-point shortest-path queries can be answered very fast. Computing shortest paths is a well studied problem, but exact algorithms do not scale to huge graphs encountered on the web, social networks, and other applications.
In this paper we focus on approximate methods for distance estimation, in particular using landmark-based distance indexing. This approach involves selecting a subset of nodes as landmarks and computing (offline) the distances from each node in the graph to those landmarks. At runtime, when the distance between a pair of nodes is needed, we can estimate it quickly by combining the precomputed distances of the two nodes to the landmarks.
We prove that selecting the optimal set of landmarks is an NP-hard problem, and thus heuristic solutions need to be employed. Given a budget of memory for the index, which translates directly into a budget of landmarks, different landmark selection strategies can yield dramatically different results in terms of accuracy. A number of simple methods that scale well to large graphs are therefore developed and experimentally compared. The simplest methods choose central nodes of the graph, while the more elaborate ones select central nodes that are also far away from one another. The efficiency of the suggested techniques is tested experimentally using five different real world graphs with millions of edges; for a given accuracy, they require as much as 250 times less space than the current approach in the literature which considers selecting landmarks at random.
Finally, we study applications of our method in two problems arising naturally in large-scale networks, namely, social search and community detection.Yahoo! Research (internship
From Specifications to Behavior: Maneuver Verification in a Semantic State Space
To realize a market entry of autonomous vehicles in the foreseeable future,
the behavior planning system will need to abide by the same rules that humans
follow. Product liability cannot be enforced without a proper solution to the
approval trap. In this paper, we define a semantic abstraction of the
continuous space and formalize traffic rules in linear temporal logic (LTL).
Sequences in the semantic state space represent maneuvers a high-level planner
could choose to execute. We check these maneuvers against the formalized
traffic rules using runtime verification. By using the standard model checker
NuSMV, we demonstrate the effectiveness of our approach and provide runtime
properties for the maneuver verification. We show that high-level behavior can
be verified in a semantic state space to fulfill a set of formalized rules,
which could serve as a step towards safety of the intended functionality.Comment: Published at IEEE Intelligent Vehicles Symposium (IV), 201
Policies for allocation of information in task-oriented groups: elitism and egalitarianism outperform welfarism
Communication or influence networks are probably the most controllable of all
factors that are known to impact on the problem-solving capability of
task-forces. In the case connections are costly, it is necessary to implement a
policy to allocate them to the individuals. Here we use an agent-based model to
study how distinct allocation policies affect the performance of a group of
agents whose task is to find the global maxima of NK fitness landscapes. Agents
cooperate by broadcasting messages informing on their fitness and use this
information to imitate the fittest agent in their influence neighborhoods. The
larger the influence neighborhood of an agent, the more links, and hence
information, the agent receives. We find that the elitist policy in which
agents with above-average fitness have their influence neighborhoods amplified,
whereas agents with below-average fitness have theirs deflated, is optimal for
smooth landscapes, provided the group size is not too small. For rugged
landscapes, however, the elitist policy can perform very poorly for certain
group sizes. In addition, we find that the egalitarian policy, in which the
size of the influence neighborhood is the same for all agents, is optimal for
both smooth and rugged landscapes in the case of small groups. The welfarist
policy, in which the actions of the elitist policy are reversed, is always
suboptimal, i.e., depending on the group size it is outperformed by either the
elitist or the egalitarian policies
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