文章分类 - 影响力最大化
摘要：def celfpp(G)networkx, k): # 开始celfpp S = set() # Note that heapdict is min heap and hence add negative priorities for # it to work. Q = heapdict() la
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摘要：ic with networkx def ic_with_networkx(G_networkx, S, target_nodes, p, mc): """ Input: graph object, set of seed nodes, propagation probability and the
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摘要：import diffusion from heapdict import heapdict class Node(object): def __init__(self, node): self.node = node self.mg1 = 0 self.prev_best = None self.
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摘要：- Cardinality constrained submodular maximization for random streams - An Efficient Framework for Balancing Submodularity and Cost - Regularized Submo
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摘要：Cascade Models. We focus on two widely adopted diffu- sion models: the Independent Cascade (IC) model and Linear Threshold (LT) model. Both models sha
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摘要：先从矩阵的方面来认识。 矩阵拟阵记录了一个矩阵中向量之间的线性无关关系。矩阵的所有其它性质与拟阵无关。所以拟阵被定义为一个二元组
摘要：In this paper, we focus on the case where the functions
摘要：The IM problem is characterized by the triple
摘要：We propose a novel semi-bandit feedback model based on pairwise influence (Section 4). Our feedback model is weaker than the edge-level feedback propo
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摘要：Unfortunately, knowledge of the underlying diffusion model and its parameters is essential for the existing IM algorithms to perform well. For example
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摘要：Deep Set Prediction Networks, NeurIPS 2019 A Multi-Feature Diffusion Model: Rumor Blocking in Social Networks, TON
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摘要：Optimizing Generalized Gini Indices for Fairness in Rankings, SIGIR 2022 Individual Fairness for Graph Neural Networks: A Ranking based Approach, KDD
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摘要：Decentralized Planning in Stochastic Environments with Submodular Rewards, AAAI 2017 Multi-agent active perception with prediction rewards, NeurIPS 20
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摘要：Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted Rewards Group-Fairness in Influence Maximization Fa
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