Cost-effective lazy-forward
WebNov 1, 2016 · Leskovec et al. [37] put forward an improved greedy method by introducing a “Cost-Efficient Lazy Forward” (CELF) scheme. The CELF method can speed up the greedy algorithm by 700 times almost. ... Then, a cost-effective competition (CEC) problem is proposed by formulating the number of votes and the recruiting costs as two optimization ... Webseeds, which was referred to as the "Cost-Effective Lazy Forward" (CELF) scheme. The CELF optimization used the submodularity property. Chen et al. proposed a scalable heuristic called LDAG for the LT model [6]. They constructed local directed acyclic graphs (DAGs) for each node and considered influence only within it.
Cost-effective lazy-forward
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WebCost-Effective Lazy Forward (CELF) optimization that reduces the computation cost of the influence spread using sub-modularity property of the objective function. Chen et al. [4] proposed new greedy algorithms for independent cascade and weighted cascade models. They made the greedy algorithm faster by combining their algorithms with CELF. WebCost-effective definition, producing optimum results for the expenditure. See more.
Web(Leskovec et al., 2007) have proposed an effective technique over the greedy algorithm called cost-effective lazy forward (CELF), which is many times better than greedy … WebProceedings of the Fourteenth International AAAI Conference on Web and ... ... and ()=). ...
WebNov 21, 2024 · Leskovec et al. proposed an approach named cost-effective lazy forward (CELF), which is 700 times more efficient than the greedy algorithm. CELF uses diminishing returns property of a sub-modular function of cascade influence. WebFind 91 ways to say COSTEFFECTIVE, along with antonyms, related words, and example sentences at Thesaurus.com, the world's most trusted free thesaurus.
WebSep 7, 2024 · Cost Effective Lazy Forward (CELF) Algorithm. The CELF algorithm was developed by Leskovec et al. (2007). Although the Greedy algorithm is much quicker than solving the full problem, it is still very slow …
WebLeskovec et.al. first build up a method called Cost-Effective lazy Forward (CELF) for the BIM, which uses the submod-ularity property to speed up the algorithm and it is much … purpose of man according to the bibleWebeach round and proposed the “Cost-Effective Lazy Forward” (CELF) scheme. Experimental results demonstrate that CELF optimization could achieve as much as 700-time speed-up in selecting seeds. However, even with CELF mechanism, the number of candidate seeds is still large. Recently, Goyal et al. proposed CELF++ [6] that has been … purpose of managed careWebApr 2, 2024 · seed sets. Leskovec et al. [28] proposed cost-effective lazy forward selection (CELF), which, according to the sub-modularity of the influence maximization objective, achieves near-optimal placements. Chen et al. proposed the NewGreedyIC algorithm, which can decrease the time costs and optimize the diffusion of influence [23]. purpose of mandatory reportingWebet al. present an optimization in selecting new seeds, which is referred to as the “Cost-Effective Lazy Forward” (CELF) scheme. The CELF optimization uses the submodularity property of the influence maximization objective to greatly reduce the number of evaluations on the influence spread of vertices. security finance dickson tennesseeWebJan 22, 2024 · In this paper, we analyze the influence maximization problem in temporal social networks and present a greedy-based on the latency-aware independent cascade (GLAIC) algorithm enhanced by cost-effective lazy forward optimization based on the latency-aware independent cascade model to capture the dynamic aspect of real-world … purpose of malate aspartate shuttleWebinfluence propagation using the Cost-Effective Lazy Forward (CELF) technique [4]. The unnecessary marginal gain re-calculation is avoided providing a more vivid and better evaluation by the improved CELF algorithm called CELF++. The greedy algorithm - Practical Partitioning and Seeding (PrPaS), is focused towards ... security finance dickson tnWebAug 10, 2024 · We develop a version of Cost Effective Lazy Forward optimization with GLIE instead of simulated influence estimation, surpassing the benchmark for influence maximization, although with a computational overhead. To balance the time complexity and quality of influence, we propose two different approaches. purpose of manipulation check in anova