for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks. We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform

outperform word-based models. Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations

the R*-tree which incorporates a combined optimization of area, margin and overlap of each enclosing rectangle in the directory. Using our standardized testbed in an exhaustive performance comparison, it turned out that the R*-tree clearly outperforms the existing R-tree variants. Guttman's linear

implementations of modified algorithms on parallel computers with distributed memory. Performance analysis carried out on an Intel Hypercube shows that parallel computing becomes gradually more and more efficient the longer the sequences are.

and relevance models. We carried out experiments to compare time-based language models to heuristic techniques for incorporating document recency in the ranking. Our results show that time-based models perform as well as or better than the best of the heuristic techniques.

resorted to a local search heuristic. Surprisingly it turned out that for the proposed AT&T WorldNet backbone, we found weight settings that performed

The language modeling approach to retrieval has been shown to perform well empirically. One advantage of this new approach is its statistical foundations. However, feedback, as one important component in a retrieval system, has only been dealt with heuristically in this new retrieval approach

. The avoidance maneuver a { a specific time is thus computed by selecting robot’s velocities out of that set. ~’lle set [If all avoid inp, velocities is redllced to the dynamica]]y fcasib]e maneuvers by considering the robot’s acceleration constraints. This computation is re])eatecl at regular time intro vals

prevalent viral marketing in largescale online social networks. Prior solutions, such as the greedy algorithm of Kempe et al. (2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this paper, we design a

the combination that provides the greatest reduction in tree size. Results indicate that the history heuristic and transposition tables significantly out-perform other alpha-beta enhancements in application generated game trees. For trees up to depth 8, when taken together, they account for over 99